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This article was downloaded by [University of Tennessee Knoxville]On 21 December 2014 At 2224Publisher Taylor amp FrancisInforma Ltd Registered in England and Wales Registered Number 1072954 Registered office Mortimer House37-41 Mortimer Street London W1T 3JH UK
Journal of Information and Optimization SciencesPublication details including instructions for authors and subscription informationhttpwwwtandfonlinecomloitios20
An experimental analysis of the CGPS algorithm for thethree-machine flow shop scheduling with minimummakespan criterionHui-Chin Tang aa Department of Industrial Engineering and Management Cheng Shiu Institute ofTechnology Kaohsiung 83305 Taiwan Republic of ChinaPublished online 18 Jun 2013
To cite this article Hui-Chin Tang (2002) An experimental analysis of the CGPS algorithm for the three-machine flowshop scheduling with minimum makespan criterion Journal of Information and Optimization Sciences 232 323-344 DOI10108002522667200210699531
To link to this article httpdxdoiorg10108002522667200210699531
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An experimental analysis of the CGPS algorithm for the three-machine flow shop scheduling with minimum makespan criterion
Hui-Chin Tang
Department of Industrial Engineering and Management
Cheng Shiu Institute of Technology
Kaohsiung 83305
Taiwan
Republic of China
ABSTRACT The algorithm developed by Chen Glass Potts and Strusevich (CGPS) is by far
the best one in terms of the worst-case perfurmance ratio for the three-machine flow shop scheduling (3FSS) with minimum makespan criterion This paper focuses on the experimental analysis of the CGPS algorithm Three simulation experiments are conshyducted to compare and evaluate its performance in terms of six evaluation measures It is shown that the CGPS algorithm is not empirically preferred for the 3FSS with minimum makespan criterion
1 INTRODUCTION
Since the publication of Johnsons 1954 paper on the two-machine flow shop scheduling (FSS) [6] the growth in interest in the FSS problem has been quite dramatic [3 4 8 12] The FSS is a productionshyplanning problem that schedules 11 jobs on m sequential machines At any time each machine processes at most one job and each job is processed on at most one machine The uninterrupted processing time of each job on each machine is given Among many regular criteria this paper is concerned with the makespan Since it is the easiest crishyterion to use and represents other beneficial characteristics such as maximum machine utilization of a production line [4] Thus this paper considers the FSS with minimum makespan criterion
An intfgtresting special case is the three-machine FSS (3FSS) [2 17] The 3FSS has many properties One property is that there exists an
Journal of Information amp Optimization Sciences Vol 23 (2002) No2 pp 323-344 copy Analytic Publishing Co 0252middot266702 $200+25
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324 H C TANG
optimal schedule that is a permutation schedule viz schedule with the same processing order on each machine [3 12] Another property is that the 3FSS with minimum makespancriterion is a strongly NPshyhard problem [5 9] while the two-machine FSS with minimum makespan criterion has a computational time complexity of O(n log n) [6J Therefore much research has been devoted to proposing heuristic algorithms yielding near-optimal schedules in a quick time Two heuristic algorithms are distinguished constructive algorithms and improvement algorithms For more details see Smutnicki [14] Sullman [15] and the references cited there In this paper we restrict overselves to the constructive algorithms for the 3FSS with minimum makespan criterion
Moreover the measures of a heuristic algorithm performance can be investigated either experimentally or analytically (worst-case analysis or probability analysis) As the worst-case analysis is concerned the heuristic algorithm developed by Chen Glass Potts and Strusevich (CGPS) [2J is the best one so far This is very inspiring However no experimental analysis of its performance has been reported to date
The primary purpose of this paper is to measure the CGPS alshygorithm performance by the experimental analysis In the following sections we first recall the CGPS algorithm for the 3FSS with minimum makespan criterion A number of evaluation measures are proposed in section 3 Detailed evaluations and comparisons are performed in section 4 Finally some concluding remarks are made
2 THE CGPS ALGORITHM
The CGPS algorithm is an O(n log n) time heuristic algorithm which has a worst-case performance ratio of 53 for the 3FSS with minimum makespan criterion There are two main stages for the CGPS algorithm Firstly the construction of an initial permutation schedule 1tl involves creating an artificial two-machine FSS problem by the machine aggregation heuristic of ROck and Schmidt [13] and then applying Johnsons algorithm [6] to obtain 1tl The second stage is attempting to improve 1tl by partitioning the middle jobs into subshyjects and then rescheduling the jobs The resulting permutation schedule is 1t2 Of the two perm utation schedules 1tl and 1t2 the CGPS algorithm is shown that at least one of these permutation schedules has makespan which is at most 53 times that of an optimal schedule
For notational convenience we adopt the same notations as Chen et al [2] Let ai hi and ci be the uninterrupted processing times of job i E N I 2 n on machines A Band C respectively Also let 1t
0 deshynote an optimal permutation schedule that minimizes the makespan
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325 ANALYSIS OF THE CGPS ALGORITHM
For N ~ N we define a(N) =L ai The notation B(N) and c(N) are iEN
defined similarly For the 1tl obtained by applying Johnsons algorithm [6] we assume that Il and v are critical jobs where 1 S Il S v s n We also assume that
Nl = I 2 Il- I Nz = Il + 1 v - I and
N3 =v+ 1 n
Then the makespan of 1tl is rewritten as
a(Nl) + aJ1 + bfl + cJ1 + c(Ns) if Il v Cmax(n l ) (1)
ja(Nl ) + aJ1 + bJ1 + b(N2) + bv + Cv + c(Ns) if Il v
In the following we present two lemmas that are useful in the subsequent analysis
V It
LEMMA 1 Cmain) ~ L ak + bv + L Ck for v == 1 2 n (2) k=l k=y
PROOF See p 893 in Chen et a1 [2] 0
Next Chen et a1 [2] applied one value of v in (2) to establish conditions under which the schedule 1tl has a worst-case performance ratio of 53
LEMMA 2 If Il and v are critical jobs in 1tgt then
(a) If Il == v then 1tl is an optimal permutation schedule (b) If Il lt v and
beNz) + minbJ1 by S 23 beN) (3)
then we have Cmax(1tl)Cmax(1t) S 53 (4)
PROOF See p 893 11 Chen et a1 [2] 0
If Il lt v and (3) is violated Chen et a1 [2] constructed a schedule 1t2 such that the better of 1tl and 1t2 has a worst-case performance ratio of 53
LEMMA 3 If Il lt v (3) is violated a(N1) + aJ1 ~ Cy + c(N3) and (nl)Cmax (1t) ~ 53 then the permutation schedule 1tz generatedCmax
by the CGPS algorithm satisfies
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326 HCTANG
Cmax(1ti)ICmax(1tmiddot) ~ 53 (5)
PROOF See p 895 in Chen et al [2) 0
Two notable observations are obtained from the CGPS algorithm One is that the desired performance (4) sometimes holds but (3) violates The other is that the schedule 1t2 does not necessarily represent an imshyprovement on 1t1 in its makespan Detailed descriptions are presented in the following
We now concern the necessary and sufficient conditions for the desired performance (4) In Lemma 2 we can recognize that condition (3) is a sufficient for the desired performance (4) However as Chen et al [2) point out (4) sometimes holds also when (3) is violated since the lower bound used in Lemma 2 is not sufficient sharp In other words (3) is a sufficient but not necessary condition for (4) Thus we propose a modification to the CGPS algorithm by changing condition (3) While we derive new lower bounds we shall be concerned with the trade-off between the sharpness of lower bounds and their computashytional requirements As indicated in Chen et al [2] the lower bounds used in the analysis are not sufficient strong Then we shall propose some stronger lower bounds with a little extra computational requirement One way improving upon lower bound of the optimal schedule is to apply two values of v in (2)
LEMMA 4 If fl and v are critical jobs in 1t1 and fl lt v then
(a) Cmai1tmiddot) ~ plusmnak + i Ck + max b~ + ~ Ck plusmnak + bv ) (6) kl kv k=1l k=~+l
(b) We can replace (3) by
b(Ni) + bll + bv - maX[bll + ~ Ck plusmnak + b) ~ 23 beN) (7) k=~ k=IHl
(c) If condition (3) holds then (7) holds
PROOF After applying (2) with v fl and v v gives
Inequality (6) is now derived
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327 ANALYSIS OF THE CGPS ALGORITHM
To prove part (b) we subtract (6) from (1) to obtain
Cmai 1t l) - Cmai 1tmiddot) $ a(N1) + all + bit + b(Nz + by + cy+ c(Na)
Substituting (7) yields
Cmai1tl) - Cmai1tmiddot)$ 23 b(N)
The desired inequality (4) is now deduced from C (1t) b(N) Somax that we can replace (3) by (7)
Finally since
b(N2) + bit + bv - maxi bit + ~ Ck I ak + by ) w1t kdegIt+1
$ b(N2) + bit + by - max blL by)
== b(N2) + min bit by
it follows that if (3) holds then (7) holds This completes the proof of the lemma 0
Another approach for deriving a sharper lower bound of the optimal schedule is using (2) From the proof of Lemma 1 in Chem et al [2] the lower bound of an optimal schedule is
(8)
Iy n )where max L (Jk + L Pk 11 $ v $ n is the makespan of the artificial
k=l k=y
two-machine FSS with processing times Ctk == ak + bk and Pk = bk + Ck
for kEN It follows that condition (3) can be replaced by
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328 H C TANG
It is recognized that the lower bound (8) considers all values of v in (2) but the lower bound (6) only considers two of its values This implies that the lower bound (8) is stronger than (6) Therefore condition (9) is less restrictive than condition (7) This result is stated formally below
LEMMA 5 If condition (7) holds then (9) holds
Therefore among the three conditions condition (9) is the least restrictive from Lemma 4 and Lemma 5
The second notable observation is that the schedule 1t2 does not always improve the makespan of the schedule 1tl In other words the makespan of1t2 sometimes is larger than or equal to the makespan Of1tl
Taking into consideration the above pieces of information our conclusion is that eight cases arise through the various possible comshybinations For condition (3) ~s an example these cases are
(C1) 11 =v
(C2) 11 lt v and (3) holds
(C3) 11 lt v (3) is violated Cmai1tl) gt Cmai1t0 and (4) holds
(C4) 11 lt v (3) is violated Cmai1tl) gt Cmai1t2) and (4) is violated (10)
(C5) 11 lt v (3) is violated Cmai1tl) = Cmai1t2) and (4) holds
(C6) 11 lt v (3) is violated Cmai1tl) = Cmai1t0 and (4) is violated
(C7) 11 lt v (3) is violated Cmai1tl) lt Cmai1t2) and (4) holds
(C8) 11 lt v (3) is violated Cmai1tl) lt Cmai1t2) and (4) is violated
Similarly for other two proposed conditions we replace (3) by (7) and (9) respectively
3 EVALUATION MEASURES
To evaluate the performance of the CGPS algorithm this paper provides a number of evaluation measures in three respects The first respect refers to the effectiveness of the CGPS algorithm The second respect is concerned with the comparison of the three conditions satisfyshying the desired performance (4) The final respect is apout comparing with the heuristics developed by Nawaz Enscore and Ham (NEH) [10] and by Campbell Dudek and Smith (CDS) [1] which are the best two among the constructive algorithms for the FSS with minimum makespan criterion
Firstly to measure the effectiveness of the CGPS algorithm two evaluation measures are adopted In Lemma 2 for the case that
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329 ANALYSIS OF THE CGPS ALGORITHM
l == v an optimal schedule is found from the CGPS algorithm It follows that the percentage of schedules that are equal to the optimal ones is used to measure its effectiveness This proportional optimum is defined as
P [(C1)Tl 100 (11)
where (C1) denotes the number of times the schedule Xl that is equal to an optimal one in T problem instances Another evaluation measshyure is the empirical average-case performance ratio For the algorithm A and problem instance Z its quality is measured by the ratio
(12)
It is recognized that the worst-case performance ratio provides a performance guarantee which is valid for all problem instances As stated in Chen et al [2] the CGPS algorithm is by far the best one with the worst-case performance ratio as the performance measure However the Operations Research practitioners also concern the comshyputational experience of the CGPS algorithm Thus we adopt the empirical average-case performance ratio
T
~A L llA(Zi)T (13) i=l
to measure its performance empirically For notational convenience the argument Z will be omitted in Cmaix Z) and Cmax(x Z) if it is not necessary in the following
Secondly to compare conditions (3) (7) and (9) satisfying the deshysired performance (4) three evaluation measures are proposed We only consider condition (3) since the other conditions are treated simishylarly It is recognized that the desired performance (4) sometimes holds but condition (3) violates This implies that the percentage of equivalent relation between condition (3) and the desired performance (4) can be used as an evaluation measure This proportional equivashylency can be defined as
i _[ (C2) + (C4) +(C6) + (C8) ] E - (C2)+(C3)+(C4)+(C5)+(C6)+(C7)+(C8) 100
for i == 3 7 9 (14)
where (C2) + (C4) + (C6) + (C8) is thee number of times the equivalent relation between the desired performance (4) and condition (3) The other evaluation measures concern an improvement on the makespan of the schedule Xl Two evaluation measures are distinshy
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330 HCTANG
guished One is the relative error of the schedule 1tl from the schedule 1tH in terms of the 11Z)
i [ Cmax(1tH Z)Cmax(1t Z) 1R 1- 100 Cmax(1tl Z)CmaxC1t Z)
Cmax(1tH Z) ] (15) 1 - C ( l Z) 100 for pound 3 7 9[ 1tmax
The other is an improvement on the makespan of the schedule 1t1 in terms of the problem instance The definition of this proportional imshyprovement is
Ii _ [ (C3) + (C4) ] - (C3) +(C4) +(C5) +(C6) +(C7) +(C8) 100
for i 3 7 9 (16)
where (C3) + (C4) is the number of times the schedule 1tH that imshyproves the makespan of the schedule 1tl
Finally in concern with the comparison of the CGPS NEH and CDS algorithms two evaluation measures are adopted One measure is based on the empirical average-case performance ratio (13) The other measure is that the percentage of problem instances that the A algorithm is superior to the B algorithm in its makespan This proporshytional superiority can be defined as
A _[ (A) ] (17)SB - (A) +(AB) + (B) 100
where (A) and (B) are the number of times the makespan better by A and B algorithms respectively and (AB) is the number of times the makespan equal by A and B algorithms Denote E~ the percentage of problem instances that the A algorithm is equal to the B algorithm
Therefore there are six evaluation measures adopted in this paper We use (11) and (13) to measure the effectiveness of the CGPS algoshyrithm To compare conditions (3) (7) and (9) satisfying the desired pershyformance (4) three evaluation measures (14) (15) and (16) are adopted To compare the CGPS algorithm with the NEH algorithm as well as the CDS algorithm two evaluation measures (13) and (17) are used
4 SIMULATION EXPERIMENTS
To get an idea of the CGPS algorithm from the empirical perspecshytive three simulation experiments are conducted in this paper The first simulation experiment examines the effectiveness of the CGPS algoshy
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331 ANALYSIS OF THE CGPS ALGORITHM
rithm The second simulation experiment concerns the empirical comparishyson of conditions (3) (7) and (9) which are the sufficient for the desired performance (4) The third simulation experiment conducts the comparison of the CGPS algorithm with the NEH and CDS algorithms
The number of machines considered is three and the numbers of jobs considered are 3 4 5 6 7 8 9 10 15 20 30 40 50 100 200 300 400 500 600 700 800 900 and 1000 The problem size is dishyvided into two classes One is the small problem for the 3 4 5 6 7 8 9 and 10 job problems The other is the large problem for the 15 20 30 40 50 100 200 300 400 500 600 700 800 900 and 1000 job problems One hundred problems are generated for each job The processing times for each problem are randomly generated integers uniformly distributed between 1 and 100 A more portable random number generator Xn == 16807xH(mod 231
- 1) is adopted using same to generate the random values for each problem The whole simushy
lation experiments are subjected to extensive testing 13800 problems A number of evaluation measures described in previous section
are used for the three simulation experiments We adopt the evaluation measures (11) and (13) for the first simulation experiment (14) (15) and (16) for the second simulation experiment (13) and (17) for the third simulation experiment respectively The whole computation is conducted on a 266 MHz Pentinum II PC using Microsoft Visual C++ compiler under Microsoft Windows 98 operating system
To obtain an optimal schedule for the empirical average-case pershyformance ratio the branch-and-bound algorithm with two-machine lower bound rule proposed by Tang [16] is applied This proposed twoshymachine lower bound is a modification to the algorithms of Lageweg et al [7] and Potts [11] and is shown to be more efficient Whenever a problem is not solved after 100000 nodes have been generated comshyputation is stopped In this case the optimal makespan is replaced by the best-known makespan Thus the empirical average-case pershyformance ratio is underestimated
The first simulation experiment is concerned with the effectiveness of the CGPS algorithm Detailed results are presented in Table 1 where Min denotes the minimum value of proportional optimum or empirical average-case performance ratio Max and Avg the corresshyponding maximum and average values respectively The proportional optimum is 503478 on the average In other words 503478 of the initial permutation schedules generated by the CGPS algorithm are the optimal schedules especially for the large problems the empirical average-case performance ratio is concerned Table 1 lists the percentage POP of problem instances that the optimal schedules are found by the
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332 H C TANG
branch-and-bound algorithm with Tangs two-machine lower bound rule [16] The average percentage Popt is 962609 Especially in the case of small problems the branch-and-bound algorithm can always find the optimal schedules within 100000 nodes generated Therefore it is appropriate to adopt the empirical average-ca~e performance ratio to measure the effectiveness of the CGPS algorithm The empirical average-case performance ratio of the initial permutation schedule is in the range of 10011 and 10355 and is 10156 on the average While the empirical average-case performance ratio of the CGPS algorithm is in the range of 10001 and 10282 and is 10115 on the average These values are always smaller than the performance guarantee 53 of the CGPS algorithm
Table 1 Results of simulations for the effectiveness of the COPS
Job P Popt ~1t1
3 59 100 10139 4 53 100 10185 10148 5 40 100 10309 10264 6 40 100 10317 10282 7 33 100 10318 10262 8 26 100 10355 10270 9 36 100 10355 10282 10 35 100 10337 10255 15 44 84 10299 10243 20 42 82 10300 10183 30 53 90 10157 10106 40 51 94 10132 10085 50 48 95 10128 10065 100 51 93 10083 10032 200 56 97 10040 10017 300 69 99 10021 10007 400 62 98 10022 10004 500 57 96 10018 10002 600 57 99 10017 10002 700 64 96 10013 10001 800 60 99 10012 10002
(Table 1 Contd)
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333 ANALYSIS OF THE CGPS ALGORITHM
lOOO 60 97 10001
Small problems Max
Avg 59
402500 lOO lOO
10355 10289
10139 10282 10238
Large problems
Min Max
42 82 10011 10300
10001 10243 10050
Overall problems Max 69
503478 100
922609 10355 10156
10001 10282 10115
The second experiment refers to the comparison of conditions (3) (7) and (9) in terms of the proportional equivalency (14) the relative error (15) and the proportional improvement (16) Table 2 gives the reshysults Notably the number of times of the cases C3 C4 C5 C6 C7 and C8 for conditions (7) and (9) are all zeros In other words the CGPS algorithm with condition (7) or (9) only generates the initial permutashytion schedule It follows that the proportional equivalency is lOO the relative error is zero and the proportional improvement is not availshyable (NA) for each problem These results indicate that the initial pershymutation schedule never violates the worst-case performance ratio of 53 if we adopt the CGPS algorithm with condition (7) or (9)
In the case of condition (3) viz the original CGPS algorithm the proportional equivalency is 2695 on the average This value indicates that 7305 of the desired performance (4) holds also when condition (3) is violated Therefore condition (3) is too restrictive especially for the large problems In concern with the improvement on the makesshypan of 1tl the relative error is 03940 and the proportional improveshyment is 647152 on the average This implies that 647154 of the permutation schedule 1t2 has improved on the initial permutation schedule in its makespan However the quality of improvement made by 1t2 over 1tl is only 03940 in terms of the empirical average-case performance ratio Therefore condition (3) in the CGPS algorithm is more restrictive and the improvement in the average-case performance ratio is so marginal from the empirical point of view
The third experiment concerns the comparison of the CGPS algoshyrithm with the NEH algorithm as well as the CDS algorithm Comparashytive results are shown in Table 3 On basis of the empirical
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ltgtl
ltgtl
Tab
le 2
C
om
para
tiv
e r
esu
lts
for
co
nd
itio
ns
(3)
(7)
an
d (
9)
--------------shy
Job
C
ondi
tion
s E
i R
i Ii
C
1 C
2 C
3 C
4 C
5 C
6 C
7 C
8 -----shy
(3)
59
39
0 0
2 0
0 0
951
220
0
0000
000
00
3 (7
) 59
41
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 59
41
0
0 0
0 0
0 10
000
00
0
0000
NA
-shy
(3)
53
39
4 0
0 0
4 0
829
787
0
3633
500
000
4 (7
) 53
47
0
0 0
0 0
0 10
000
00
0
0000
NA
53
47
0
0 0
0 0
0 10
000
00
0
0000
NA
---shy
-----------shy
(3)
40
44
9 0
1 0
6 0
733
333
0
4365
562
500
5 (7
) 40
60
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 40
eo
0
0 0
0 0
0 10
000
00
0
0000
NA
(3
) 40
4
7
5 0
0 0
8 0
783
333
0
3392
384
600
6 (7
) 40
6
0
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
40
60
0
0 0
0 0
0 10
000
00
0
000
0
NA
-----------shy
(3)
33
37
10
0
0 0
20
0 55
223
9
054
27
33
330
0
7 (7
) 33
6
7
0 0
0 0
0 0
100
0000
000
00
N
A
p (9
) 33
6
7
0 0
0 0
0 0
100
0000
000
00
N
A
0 ------~
(Tab
le 2
Con
td)
gtshy Z
C
)
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Con
diti
ons
~ z
Job
E
i R
i Ii
gt
C
l C
2 C
3 C
4 C
5 C
6 C
7 C
8 S CD
26
40
16
0 1
0 17
0
540
541
0
8209
470
600
CD
0 gt1j
8 (7
) 26
74
0
0 0
0 0
0 10
000
00
0
0000
NA
gt-3 r
(9)
26
74
0 0
0 0
0 0
100
0000
000
00
N
A
ttl
()
36
29
12
0 1
0 22
0
453
125
0
7050
342
900
0 0
9
(7)
36
64
0 0
0 0
0 0
100
0000
000
00
N
A
CD
t
(9)
36
64
0 0
0 0
0 0
100
0000
000
00
N
A
0 ~
0 0(3
) 35
29
19
0
0 0
17
0 44
615
4
079
33
52
780
0
gt-3 r10
(7
) 35
65
0
0 0
0 0
0 10
000
00
0
0000
NA
s
(9
) 35
65
0
0 0
0 0
0 10
000
00
0
0000
NA
(3
) 44
12
20
0
0 0
24
0 21
428
6
054
37
45
450
0
15
(7)
44
56
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
44
56
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
42
9 27
0
0 0
22
0 15
517
2
113
59
55
100
0
20
(7)
42
58
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
42
58
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
53
6 19
0
0 0
22
0 12
766
0
050
21
46
340
0
30
(7)
53
47
0 0
0 0
0 0
100
0000
000
00
N
A
53
47
0 ()
0 0
0 0
100
0000
000
00
N
A
CJ
) C
J)
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w
Job
--~~
~~-
~--
Ri
Ii
w
CIl
Cl
C2
C
3 C
4
C5
C6
C7
C
8 -----------shy
(3)
51
3 21
0
1 0
24
0
61
22
4
0
46
39
456
500
40
(7
) 51
4
9
0 0
0 0
0 0
10
00
00
0
0
00
00
NA
(9
) 51
4
9
0 -----_
_
-shy
0 0
0 --shy-----~~~~
0 0
-----------shy
10
00
00
0
0
00
00
---------shy
NA
(3
) 4
8
4 33
0
0 0
15
0
769
23
0
62
20
68
75
00
50
(7
) 4
8
52
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
48
5
2
0 0
0 0
0 0
100
0000
00
00
0
N
A
51
4 3
7
0 0
0 8
0 8
16
33
05
05
8
8
22
20
0
10
0
(7)
51
49
0
0 0
0 0
0 10
000
00
0
00
00
NA
(9
) 51
4
9
0 0
0 0
0 0
100
0000
00
00
0
N
A
----------shy
(3)
56
0
35
0 0
0 9
0 0
00
00
02
29
1
79
550
0
20
0
(7)
56
4
4
0 0
0 0
0 0
100
0000
00
00
0
N
A
(9)
56
4
4
0 0
0 0
0 0
100
0000
00
00
0
N
A
69
2
24
0
0 0
5 0
64
51
6
0
13
97
82
76
00
30
0
(7)
69
31
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
(9)
69
31
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
(3)
62
1
35
0
0 0
2 0
26
31
6
0
17
96
94
59
00
~
40
0
(7)
62
3
8
0 0
0 0
0 0
10
00
00
0
0
00
00
NA
0
(9)
62
3
8
0 0
0 0
0 0
100
0000
00
00
0
N
A
fja
tJte
2-C
onat
)
gt-3
)shy z Cl
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0gtshy
Con
diti
ons
Job
E
i R
i Ii
C
l C
2 C
3 C
4 C
5 C
6 C
7 C
8 ~ rt
J gt-
lt57
0
38
0 0
0 5
0 0
0000
015
97
88
370
0
rtJ
j
500
(7)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
0 3
(9)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
tr1
tlj
0(3
) 57
1
39
0 0
0 3
0 2
3256
014
97
92
860
0
0 rtJ
60
0
(7)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
~ 57
43
0
0 0
0 0
0 10
000
00
0
0000
NA
0 0
1 34
0
gt-lt
(3)
64
0 0
1 0
277
78
0
1198
971
400
tO
3
700
(7)
64
36
0 0
0 0
0 0
100
0000
000
00
N
A
tr1 ~
(9)
64
36
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
60
2 38
0
0 0
0 0
500
00
0
0999
100
0000
800
(7)
60
40
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
60
40
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
62
0 38
0
0 0
0 0
000
00
0
0999
100
0000
900
(7)
62
38
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
62
38
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
60
0 39
0
0 0
1 0
000
0
010
99
97
500
0
1000
(7
) 60
40
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 60
40
0
0 0
0 0
0 10
000
00
0
0000
NA
w
w
(T
able
2 C
ontd
)
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------
CIJ
Con
diti
ons
CIJ
Job
E
i R
i Ii
O
J
C1
C2
C3
C4
C
5 C
6 C
7
C8
Min
44
615
4
000
00
0
0000
Sm
all
(3
) M
ax
951
220
0
8209
562
500
pro
ble
ms
Avg
66
121
6
050
01
39
021
3
_
Min
0
0000
009
99
45
450
0
Lar
ge
(3
) M
ax
214
286
1
1359
100
0000
p
rob
lem
s A
vg
605
84
0
3374
784
187
--
-_
__
---
Min
0
0000
000
00
0
0000
Ov
eral
l
(3)
Max
95
122
0
113
59
10
000
00
pro
ble
ms
Avg
26
950
0
039
40
64
715
2
tr1
()
gt-3 ~ Q
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gtshyT
able
3
z C
om
par
ativ
e re
sult
s fo
r th
e C
GP
S
CD
S
an
d N
EH
alg
ori
thm
s ~
Job 3
4
5
6
7
8
9
10
15
20
30
40
50
100
200
101
39
101
48
102
64
102
82
102
62
102
7
102
82
102
55
102
43
101
83
101
06
100
85
100
65
100
32
100
17
~CDS
100
96
101
28
102
14
102
32
102
46
102
15
102
04
102
04
101
78
101
53
100
99
100
63
100
55
100
35
100
17
~~--------------
~NElI
100
1
100
39
100
53
100
54
100
48
100
72
100
50
100
47
100
25
100
33
100
08
100
05
100
06
100
02
100
01
(Tab
le 3
Con
td)
lfl
gt-
lfl
0 tJ r t=J
0 0 i
lfl gtshy r 0 0 ~ r $
w
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-----
---
co
jgt
~CGPS
~CDS
~NE
H 0
300
100
07
100
11
100
00
400
100
04
100
06
100
00
500
100
02
100
06
100
00
600
100
02
100
03
100
00
700
100
01
100
02
100
00
800
100
02
100
02
100
00
900
100
01
100
04
100
00
1000
1
0001
1
0003
1
0000
Min
1
0139
1
0096
1
0010
Sm
all
pro
ble
ms
Max
1
0282
1
0246
1
0072
Avg
1
0238
1
0192
1
0047
Min
1
0001
1
0002
1
0000
Lar
ge
pro
ble
ms
Max
1
0243
1
0178
1
0033
Avg
1
0050
1
0042
1
0005
Min
1
0001
1
0002
1
0000
r
Ov
eral
l p
rob
lem
s M
ax
102
82
102
46
100
72
0 -3A
vg
101
15
100
95
100
20
p Z
(Tab
le 3
Con
td)
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lgtT
able
3 (
Con
td)
Z
--------
_shy
SCG
PS
EQ
CG
PS
SCD
S SC
GP
S E
QC
GP
S SN
EH
SN
EH
EQ
IjE
H
SCD
S --
~ Jo
b C
DS
CDS
CG
PS
NEH
N
EH
CG
PS
CD
S CD
S N
EH
en
en
3 0
91
9
0
83
17
12
88
0
0 gtzj
4 2
8
7
11
2
70
28
21
76
3
gt-l
[1j
5 2
82
16
4
58
38
28
66
6
0 0 1
j6
3
77
20
1
51
48
40
56
4
en
7 5
75
20
5
43
52
45
50
5
~ 0 0
8 6
70
24
8
35
57
44
47
9
~
gt-l
9 5
6
7
28
2
40
58
46
46
8
10
7
68
25
5
40
55
42
50
8
15
7
70
23
2
45
53
46
50
4
20
8
65
27
4
46
50
37
58
5
30
8
74
18
0
57
43
35
63
2
40
7
72
21
3
53
44
29
62
9
50
13
62
25
4
49
47
31
59
10
10
0
15
6
0
25
1
52
47
27
71
2
20
0
17
64
19
2
55
43
26
68
6
30
0
12
74
14
7
63
30
17
74
9
w
(T
able
3 C
ontd
)
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Job
S
co
Ps
CD
S E
QC
GP
SC
DS
SCD
S C
GPS
SC
GP
S N
EH
E
QC
GP
SN
EH
S
NE
H
CG
PS
NE
H
SCD
S E
Q
EH
CD
S SC
DS
NE
Il
N
400
14
63
23
5
58
37
14
81
5
500
13
62
25
1
57
42
20
78
2
60
0
9
61
30
4
54
42
12
82
6
700
11
66
23
8
56
36
13
79
8
80
0
10
63
27
4
57
39
14
82
4
900
16
63
21
2
60
38
17
81
2
1000
11
61
28
4
57
39
12
82
6
Sm
all
pro
ble
ms
Min
Max
Avg
0
67
9
7
91
28
375
00
77
125
0
191
250
0
8
337
50
35
17
83
58
525
000
44
1250
12
46
347
500
46
88
598
750
0
9
537
50
Lar
ge
pro
ble
ms
Min
Max
7
17
60
74
14
30
0
8
45
63
30
53
12
46
50
82
2
10
114
00
65
3333
2
326
67
3
4000
546
000
42
0000
233
333
71
333
3
533
33
Ov
eral
l p
rob
lem
s
Min
Max
Avg
0
60
9
17
91
30
873
91
69
434
8 2
182
61
0
8
339
13
35
17
83
58
538
696
42
739
1
12
46
273
043
~~~--~--
46
0
88
10
673
478
5
3478
c 0 ~ gtshy L
0
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343 ANALYSIS OF THE CGPS ALGORITHM
average-case performance ratio the NEH algorithm is the best one folshylowed by the CDS algorithm and then by the CGPS algorithm As comshypared with the CDS algorithm in terms of the proportional superiority 87391 of problem instances better by the CGPS algorithm 694348 of problem instances equal by the CGPS and CDS algorithms and 218261 of problem instances better by the CDS algorithm on the average It follows that the CDS algorithm is better than the CGPS alshygorithm especially for the large problems Similarly the NEH algorithm is better than the CGPS algorithm and the NEH algorithm is better than the CDS algorithm especially for the small problems Therefore we have the same conclusion about these heuristics in terms of the empirishycal average-case performance ratio and proportional superiority
To sum up 503478 ofthe initial permutation schedules generated by the CGPS algorithm are the optimal schedules Among the 496522 of non-optimal schedules 647154 of the permutation schedules 1t2 improve the makespan of the initial permutation schedule However the improvement made by 1t2 is only 03940 over 1tl in terms of the empirical average-case performance ratio Moreover compared with the NEH and CDS algorithms the CGPS algorithm is the worst one in terms of the empirical average-case performance ratio and proportional superiority These imply that the CGPS algorithm is not good enough for applications
5 CONCLUSION
In this paper the 3FSS with minimum makespan criterion is considered Theoretically the heuristic algorithm proposed by Chen et a1 [2] has the best worst-case performance ratio of 53 This paper evaluates the performance of the CGPS algorithm from the empirical perspective Three simulation experiments are performed to analyze its performance in terms of the six evaluation measures on a total of 13800 problems Four conclusions can be drawn from this paper Firstly in the CGPS algorithm 503478 of the initial permutation schedules are the optimal ones 321326 of the permutation schedshyules 1t2 improve the makespan of nl and 175196 of the permutation schedules n2 do not improve their makespan Secondly the empirical average-case performance ratio of initial permutation schedule genershyated by the CGPS algorithm is always smaller than the performance guarantee 53 developed by Chen et al [2] Its empirical average-case performance ratio is in the range of 10011 and 10355 and is 10156 on the average Thirdly the permutation schedule 1t2 improves 1tl in terms of the empirical average-case performance ratio by the non-significant percentage 03940 Fourthly among the three heuristic algorithms the
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344 H C TANG
NEH algorithm is the best one in terms of both empirical average-case performance ratio and proportional superiority followed by the CDS alshygorithm and then by the CGPS algorithm Consequently the CGPS algoshyrithm is not suitable for the 3FSS with minimum makespan criterion
REFERENCES
l H G Campbell R A Dudek and M L Smith A heuristic algorithm for the n-job tn-machine sequencing problem Management Science Vol 16 (1970) pp 630-637
2 B Chen C A Glass C N Potts and V A Strusevich A new heuristic for threeshymachine flow shop scheduling Operations Research Vol 44 (1996) pp 891-898
3 R W Conway W L Maxwell and L W Miller Theory of Scheduling AddisonshyWesley Reading MA 1967
4 R A Dudek S S Panwalkar and M L Smith The lessons of flowshop scheduling research Operations Research Vol 40 (1992) pp 7-13
5 M R Garey D S Johnson and R Sethi The complexity of flowshop and jobshop scheduling Mathematics Operations Research Vol 1 (1976) pp 117-129
6 S M Johnson Optimal two- and three-stage production schedules with setup times included Naval Research Logistic Quarterly Vol 1 (1954) pp 61-68
7 B J Lageweg J K Lenstra and A H G Rinnooy Kan A general bounding scheme for the permutation flow-shop problem Operations Research Vol 26 (1978) pp 53-67
8 E L Lawler J K Lenstra A H G Rinnooy Kan and D B Shmoys Sequencing and Scheduling Algorithms and Complexity in S C Graves A H G Rinnooy
-Kan and P H Zipkin Handbooks in Operations Research and Management Science Vol 4 Logistics of Production and Inventory North Holland Amsterdam 1993 pp 445-522
9 J K Lenstra A H G Rinnooy Kan and P Brucker Complexity of machine scheduling problems Annals Discrete Mathematics Vol 1 (1977) pp 343-362
10 M Nawaz E E Enscore Jr and I Ham A heuristic algorithm for the m-machine n-job flow-shop sequencing problem OMEGA Vol 11 (1983) pp 91-95
II C N Potts An Adaptive branching rule for the permutation flow-shop problem European Journal of Operational Research Vol 5 (1980) pp 19-25
12 A H G Rinnooy Kan Machine scheduling problems classification complexity and computations Nijhoff The Hague 1976
13 H Rock and G Schmidt Machine aggregation heuristics in shop scheduling Methods of Operations Research Vol 45 (1983) pp 303-314
14 C Smutnicki Some results of the worst-case analysis for flow shop scheduling European Journal of Operational Research Vol 109 (1998) pp 66-87
15 S M A Suliman A two-phase heuristic approach to the permutation flow-shop scheduling problem International Journal of Production Economics Vol 64 (2000) pp 143-152
16 H C Tang A new lower bonding rule for permutation flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 249-257
17 H C Tang A modification to the CGPS algorithm for three-machine flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 321-331
Received January 2001
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An experimental analysis of the CGPS algorithm for the three-machine flow shop scheduling with minimum makespan criterion
Hui-Chin Tang
Department of Industrial Engineering and Management
Cheng Shiu Institute of Technology
Kaohsiung 83305
Taiwan
Republic of China
ABSTRACT The algorithm developed by Chen Glass Potts and Strusevich (CGPS) is by far
the best one in terms of the worst-case perfurmance ratio for the three-machine flow shop scheduling (3FSS) with minimum makespan criterion This paper focuses on the experimental analysis of the CGPS algorithm Three simulation experiments are conshyducted to compare and evaluate its performance in terms of six evaluation measures It is shown that the CGPS algorithm is not empirically preferred for the 3FSS with minimum makespan criterion
1 INTRODUCTION
Since the publication of Johnsons 1954 paper on the two-machine flow shop scheduling (FSS) [6] the growth in interest in the FSS problem has been quite dramatic [3 4 8 12] The FSS is a productionshyplanning problem that schedules 11 jobs on m sequential machines At any time each machine processes at most one job and each job is processed on at most one machine The uninterrupted processing time of each job on each machine is given Among many regular criteria this paper is concerned with the makespan Since it is the easiest crishyterion to use and represents other beneficial characteristics such as maximum machine utilization of a production line [4] Thus this paper considers the FSS with minimum makespan criterion
An intfgtresting special case is the three-machine FSS (3FSS) [2 17] The 3FSS has many properties One property is that there exists an
Journal of Information amp Optimization Sciences Vol 23 (2002) No2 pp 323-344 copy Analytic Publishing Co 0252middot266702 $200+25
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324 H C TANG
optimal schedule that is a permutation schedule viz schedule with the same processing order on each machine [3 12] Another property is that the 3FSS with minimum makespancriterion is a strongly NPshyhard problem [5 9] while the two-machine FSS with minimum makespan criterion has a computational time complexity of O(n log n) [6J Therefore much research has been devoted to proposing heuristic algorithms yielding near-optimal schedules in a quick time Two heuristic algorithms are distinguished constructive algorithms and improvement algorithms For more details see Smutnicki [14] Sullman [15] and the references cited there In this paper we restrict overselves to the constructive algorithms for the 3FSS with minimum makespan criterion
Moreover the measures of a heuristic algorithm performance can be investigated either experimentally or analytically (worst-case analysis or probability analysis) As the worst-case analysis is concerned the heuristic algorithm developed by Chen Glass Potts and Strusevich (CGPS) [2J is the best one so far This is very inspiring However no experimental analysis of its performance has been reported to date
The primary purpose of this paper is to measure the CGPS alshygorithm performance by the experimental analysis In the following sections we first recall the CGPS algorithm for the 3FSS with minimum makespan criterion A number of evaluation measures are proposed in section 3 Detailed evaluations and comparisons are performed in section 4 Finally some concluding remarks are made
2 THE CGPS ALGORITHM
The CGPS algorithm is an O(n log n) time heuristic algorithm which has a worst-case performance ratio of 53 for the 3FSS with minimum makespan criterion There are two main stages for the CGPS algorithm Firstly the construction of an initial permutation schedule 1tl involves creating an artificial two-machine FSS problem by the machine aggregation heuristic of ROck and Schmidt [13] and then applying Johnsons algorithm [6] to obtain 1tl The second stage is attempting to improve 1tl by partitioning the middle jobs into subshyjects and then rescheduling the jobs The resulting permutation schedule is 1t2 Of the two perm utation schedules 1tl and 1t2 the CGPS algorithm is shown that at least one of these permutation schedules has makespan which is at most 53 times that of an optimal schedule
For notational convenience we adopt the same notations as Chen et al [2] Let ai hi and ci be the uninterrupted processing times of job i E N I 2 n on machines A Band C respectively Also let 1t
0 deshynote an optimal permutation schedule that minimizes the makespan
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325 ANALYSIS OF THE CGPS ALGORITHM
For N ~ N we define a(N) =L ai The notation B(N) and c(N) are iEN
defined similarly For the 1tl obtained by applying Johnsons algorithm [6] we assume that Il and v are critical jobs where 1 S Il S v s n We also assume that
Nl = I 2 Il- I Nz = Il + 1 v - I and
N3 =v+ 1 n
Then the makespan of 1tl is rewritten as
a(Nl) + aJ1 + bfl + cJ1 + c(Ns) if Il v Cmax(n l ) (1)
ja(Nl ) + aJ1 + bJ1 + b(N2) + bv + Cv + c(Ns) if Il v
In the following we present two lemmas that are useful in the subsequent analysis
V It
LEMMA 1 Cmain) ~ L ak + bv + L Ck for v == 1 2 n (2) k=l k=y
PROOF See p 893 in Chen et a1 [2] 0
Next Chen et a1 [2] applied one value of v in (2) to establish conditions under which the schedule 1tl has a worst-case performance ratio of 53
LEMMA 2 If Il and v are critical jobs in 1tgt then
(a) If Il == v then 1tl is an optimal permutation schedule (b) If Il lt v and
beNz) + minbJ1 by S 23 beN) (3)
then we have Cmax(1tl)Cmax(1t) S 53 (4)
PROOF See p 893 11 Chen et a1 [2] 0
If Il lt v and (3) is violated Chen et a1 [2] constructed a schedule 1t2 such that the better of 1tl and 1t2 has a worst-case performance ratio of 53
LEMMA 3 If Il lt v (3) is violated a(N1) + aJ1 ~ Cy + c(N3) and (nl)Cmax (1t) ~ 53 then the permutation schedule 1tz generatedCmax
by the CGPS algorithm satisfies
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326 HCTANG
Cmax(1ti)ICmax(1tmiddot) ~ 53 (5)
PROOF See p 895 in Chen et al [2) 0
Two notable observations are obtained from the CGPS algorithm One is that the desired performance (4) sometimes holds but (3) violates The other is that the schedule 1t2 does not necessarily represent an imshyprovement on 1t1 in its makespan Detailed descriptions are presented in the following
We now concern the necessary and sufficient conditions for the desired performance (4) In Lemma 2 we can recognize that condition (3) is a sufficient for the desired performance (4) However as Chen et al [2) point out (4) sometimes holds also when (3) is violated since the lower bound used in Lemma 2 is not sufficient sharp In other words (3) is a sufficient but not necessary condition for (4) Thus we propose a modification to the CGPS algorithm by changing condition (3) While we derive new lower bounds we shall be concerned with the trade-off between the sharpness of lower bounds and their computashytional requirements As indicated in Chen et al [2] the lower bounds used in the analysis are not sufficient strong Then we shall propose some stronger lower bounds with a little extra computational requirement One way improving upon lower bound of the optimal schedule is to apply two values of v in (2)
LEMMA 4 If fl and v are critical jobs in 1t1 and fl lt v then
(a) Cmai1tmiddot) ~ plusmnak + i Ck + max b~ + ~ Ck plusmnak + bv ) (6) kl kv k=1l k=~+l
(b) We can replace (3) by
b(Ni) + bll + bv - maX[bll + ~ Ck plusmnak + b) ~ 23 beN) (7) k=~ k=IHl
(c) If condition (3) holds then (7) holds
PROOF After applying (2) with v fl and v v gives
Inequality (6) is now derived
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327 ANALYSIS OF THE CGPS ALGORITHM
To prove part (b) we subtract (6) from (1) to obtain
Cmai 1t l) - Cmai 1tmiddot) $ a(N1) + all + bit + b(Nz + by + cy+ c(Na)
Substituting (7) yields
Cmai1tl) - Cmai1tmiddot)$ 23 b(N)
The desired inequality (4) is now deduced from C (1t) b(N) Somax that we can replace (3) by (7)
Finally since
b(N2) + bit + bv - maxi bit + ~ Ck I ak + by ) w1t kdegIt+1
$ b(N2) + bit + by - max blL by)
== b(N2) + min bit by
it follows that if (3) holds then (7) holds This completes the proof of the lemma 0
Another approach for deriving a sharper lower bound of the optimal schedule is using (2) From the proof of Lemma 1 in Chem et al [2] the lower bound of an optimal schedule is
(8)
Iy n )where max L (Jk + L Pk 11 $ v $ n is the makespan of the artificial
k=l k=y
two-machine FSS with processing times Ctk == ak + bk and Pk = bk + Ck
for kEN It follows that condition (3) can be replaced by
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It is recognized that the lower bound (8) considers all values of v in (2) but the lower bound (6) only considers two of its values This implies that the lower bound (8) is stronger than (6) Therefore condition (9) is less restrictive than condition (7) This result is stated formally below
LEMMA 5 If condition (7) holds then (9) holds
Therefore among the three conditions condition (9) is the least restrictive from Lemma 4 and Lemma 5
The second notable observation is that the schedule 1t2 does not always improve the makespan of the schedule 1tl In other words the makespan of1t2 sometimes is larger than or equal to the makespan Of1tl
Taking into consideration the above pieces of information our conclusion is that eight cases arise through the various possible comshybinations For condition (3) ~s an example these cases are
(C1) 11 =v
(C2) 11 lt v and (3) holds
(C3) 11 lt v (3) is violated Cmai1tl) gt Cmai1t0 and (4) holds
(C4) 11 lt v (3) is violated Cmai1tl) gt Cmai1t2) and (4) is violated (10)
(C5) 11 lt v (3) is violated Cmai1tl) = Cmai1t2) and (4) holds
(C6) 11 lt v (3) is violated Cmai1tl) = Cmai1t0 and (4) is violated
(C7) 11 lt v (3) is violated Cmai1tl) lt Cmai1t2) and (4) holds
(C8) 11 lt v (3) is violated Cmai1tl) lt Cmai1t2) and (4) is violated
Similarly for other two proposed conditions we replace (3) by (7) and (9) respectively
3 EVALUATION MEASURES
To evaluate the performance of the CGPS algorithm this paper provides a number of evaluation measures in three respects The first respect refers to the effectiveness of the CGPS algorithm The second respect is concerned with the comparison of the three conditions satisfyshying the desired performance (4) The final respect is apout comparing with the heuristics developed by Nawaz Enscore and Ham (NEH) [10] and by Campbell Dudek and Smith (CDS) [1] which are the best two among the constructive algorithms for the FSS with minimum makespan criterion
Firstly to measure the effectiveness of the CGPS algorithm two evaluation measures are adopted In Lemma 2 for the case that
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329 ANALYSIS OF THE CGPS ALGORITHM
l == v an optimal schedule is found from the CGPS algorithm It follows that the percentage of schedules that are equal to the optimal ones is used to measure its effectiveness This proportional optimum is defined as
P [(C1)Tl 100 (11)
where (C1) denotes the number of times the schedule Xl that is equal to an optimal one in T problem instances Another evaluation measshyure is the empirical average-case performance ratio For the algorithm A and problem instance Z its quality is measured by the ratio
(12)
It is recognized that the worst-case performance ratio provides a performance guarantee which is valid for all problem instances As stated in Chen et al [2] the CGPS algorithm is by far the best one with the worst-case performance ratio as the performance measure However the Operations Research practitioners also concern the comshyputational experience of the CGPS algorithm Thus we adopt the empirical average-case performance ratio
T
~A L llA(Zi)T (13) i=l
to measure its performance empirically For notational convenience the argument Z will be omitted in Cmaix Z) and Cmax(x Z) if it is not necessary in the following
Secondly to compare conditions (3) (7) and (9) satisfying the deshysired performance (4) three evaluation measures are proposed We only consider condition (3) since the other conditions are treated simishylarly It is recognized that the desired performance (4) sometimes holds but condition (3) violates This implies that the percentage of equivalent relation between condition (3) and the desired performance (4) can be used as an evaluation measure This proportional equivashylency can be defined as
i _[ (C2) + (C4) +(C6) + (C8) ] E - (C2)+(C3)+(C4)+(C5)+(C6)+(C7)+(C8) 100
for i == 3 7 9 (14)
where (C2) + (C4) + (C6) + (C8) is thee number of times the equivalent relation between the desired performance (4) and condition (3) The other evaluation measures concern an improvement on the makespan of the schedule Xl Two evaluation measures are distinshy
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330 HCTANG
guished One is the relative error of the schedule 1tl from the schedule 1tH in terms of the 11Z)
i [ Cmax(1tH Z)Cmax(1t Z) 1R 1- 100 Cmax(1tl Z)CmaxC1t Z)
Cmax(1tH Z) ] (15) 1 - C ( l Z) 100 for pound 3 7 9[ 1tmax
The other is an improvement on the makespan of the schedule 1t1 in terms of the problem instance The definition of this proportional imshyprovement is
Ii _ [ (C3) + (C4) ] - (C3) +(C4) +(C5) +(C6) +(C7) +(C8) 100
for i 3 7 9 (16)
where (C3) + (C4) is the number of times the schedule 1tH that imshyproves the makespan of the schedule 1tl
Finally in concern with the comparison of the CGPS NEH and CDS algorithms two evaluation measures are adopted One measure is based on the empirical average-case performance ratio (13) The other measure is that the percentage of problem instances that the A algorithm is superior to the B algorithm in its makespan This proporshytional superiority can be defined as
A _[ (A) ] (17)SB - (A) +(AB) + (B) 100
where (A) and (B) are the number of times the makespan better by A and B algorithms respectively and (AB) is the number of times the makespan equal by A and B algorithms Denote E~ the percentage of problem instances that the A algorithm is equal to the B algorithm
Therefore there are six evaluation measures adopted in this paper We use (11) and (13) to measure the effectiveness of the CGPS algoshyrithm To compare conditions (3) (7) and (9) satisfying the desired pershyformance (4) three evaluation measures (14) (15) and (16) are adopted To compare the CGPS algorithm with the NEH algorithm as well as the CDS algorithm two evaluation measures (13) and (17) are used
4 SIMULATION EXPERIMENTS
To get an idea of the CGPS algorithm from the empirical perspecshytive three simulation experiments are conducted in this paper The first simulation experiment examines the effectiveness of the CGPS algoshy
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331 ANALYSIS OF THE CGPS ALGORITHM
rithm The second simulation experiment concerns the empirical comparishyson of conditions (3) (7) and (9) which are the sufficient for the desired performance (4) The third simulation experiment conducts the comparison of the CGPS algorithm with the NEH and CDS algorithms
The number of machines considered is three and the numbers of jobs considered are 3 4 5 6 7 8 9 10 15 20 30 40 50 100 200 300 400 500 600 700 800 900 and 1000 The problem size is dishyvided into two classes One is the small problem for the 3 4 5 6 7 8 9 and 10 job problems The other is the large problem for the 15 20 30 40 50 100 200 300 400 500 600 700 800 900 and 1000 job problems One hundred problems are generated for each job The processing times for each problem are randomly generated integers uniformly distributed between 1 and 100 A more portable random number generator Xn == 16807xH(mod 231
- 1) is adopted using same to generate the random values for each problem The whole simushy
lation experiments are subjected to extensive testing 13800 problems A number of evaluation measures described in previous section
are used for the three simulation experiments We adopt the evaluation measures (11) and (13) for the first simulation experiment (14) (15) and (16) for the second simulation experiment (13) and (17) for the third simulation experiment respectively The whole computation is conducted on a 266 MHz Pentinum II PC using Microsoft Visual C++ compiler under Microsoft Windows 98 operating system
To obtain an optimal schedule for the empirical average-case pershyformance ratio the branch-and-bound algorithm with two-machine lower bound rule proposed by Tang [16] is applied This proposed twoshymachine lower bound is a modification to the algorithms of Lageweg et al [7] and Potts [11] and is shown to be more efficient Whenever a problem is not solved after 100000 nodes have been generated comshyputation is stopped In this case the optimal makespan is replaced by the best-known makespan Thus the empirical average-case pershyformance ratio is underestimated
The first simulation experiment is concerned with the effectiveness of the CGPS algorithm Detailed results are presented in Table 1 where Min denotes the minimum value of proportional optimum or empirical average-case performance ratio Max and Avg the corresshyponding maximum and average values respectively The proportional optimum is 503478 on the average In other words 503478 of the initial permutation schedules generated by the CGPS algorithm are the optimal schedules especially for the large problems the empirical average-case performance ratio is concerned Table 1 lists the percentage POP of problem instances that the optimal schedules are found by the
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332 H C TANG
branch-and-bound algorithm with Tangs two-machine lower bound rule [16] The average percentage Popt is 962609 Especially in the case of small problems the branch-and-bound algorithm can always find the optimal schedules within 100000 nodes generated Therefore it is appropriate to adopt the empirical average-ca~e performance ratio to measure the effectiveness of the CGPS algorithm The empirical average-case performance ratio of the initial permutation schedule is in the range of 10011 and 10355 and is 10156 on the average While the empirical average-case performance ratio of the CGPS algorithm is in the range of 10001 and 10282 and is 10115 on the average These values are always smaller than the performance guarantee 53 of the CGPS algorithm
Table 1 Results of simulations for the effectiveness of the COPS
Job P Popt ~1t1
3 59 100 10139 4 53 100 10185 10148 5 40 100 10309 10264 6 40 100 10317 10282 7 33 100 10318 10262 8 26 100 10355 10270 9 36 100 10355 10282 10 35 100 10337 10255 15 44 84 10299 10243 20 42 82 10300 10183 30 53 90 10157 10106 40 51 94 10132 10085 50 48 95 10128 10065 100 51 93 10083 10032 200 56 97 10040 10017 300 69 99 10021 10007 400 62 98 10022 10004 500 57 96 10018 10002 600 57 99 10017 10002 700 64 96 10013 10001 800 60 99 10012 10002
(Table 1 Contd)
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333 ANALYSIS OF THE CGPS ALGORITHM
lOOO 60 97 10001
Small problems Max
Avg 59
402500 lOO lOO
10355 10289
10139 10282 10238
Large problems
Min Max
42 82 10011 10300
10001 10243 10050
Overall problems Max 69
503478 100
922609 10355 10156
10001 10282 10115
The second experiment refers to the comparison of conditions (3) (7) and (9) in terms of the proportional equivalency (14) the relative error (15) and the proportional improvement (16) Table 2 gives the reshysults Notably the number of times of the cases C3 C4 C5 C6 C7 and C8 for conditions (7) and (9) are all zeros In other words the CGPS algorithm with condition (7) or (9) only generates the initial permutashytion schedule It follows that the proportional equivalency is lOO the relative error is zero and the proportional improvement is not availshyable (NA) for each problem These results indicate that the initial pershymutation schedule never violates the worst-case performance ratio of 53 if we adopt the CGPS algorithm with condition (7) or (9)
In the case of condition (3) viz the original CGPS algorithm the proportional equivalency is 2695 on the average This value indicates that 7305 of the desired performance (4) holds also when condition (3) is violated Therefore condition (3) is too restrictive especially for the large problems In concern with the improvement on the makesshypan of 1tl the relative error is 03940 and the proportional improveshyment is 647152 on the average This implies that 647154 of the permutation schedule 1t2 has improved on the initial permutation schedule in its makespan However the quality of improvement made by 1t2 over 1tl is only 03940 in terms of the empirical average-case performance ratio Therefore condition (3) in the CGPS algorithm is more restrictive and the improvement in the average-case performance ratio is so marginal from the empirical point of view
The third experiment concerns the comparison of the CGPS algoshyrithm with the NEH algorithm as well as the CDS algorithm Comparashytive results are shown in Table 3 On basis of the empirical
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ltgtl
ltgtl
Tab
le 2
C
om
para
tiv
e r
esu
lts
for
co
nd
itio
ns
(3)
(7)
an
d (
9)
--------------shy
Job
C
ondi
tion
s E
i R
i Ii
C
1 C
2 C
3 C
4 C
5 C
6 C
7 C
8 -----shy
(3)
59
39
0 0
2 0
0 0
951
220
0
0000
000
00
3 (7
) 59
41
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 59
41
0
0 0
0 0
0 10
000
00
0
0000
NA
-shy
(3)
53
39
4 0
0 0
4 0
829
787
0
3633
500
000
4 (7
) 53
47
0
0 0
0 0
0 10
000
00
0
0000
NA
53
47
0
0 0
0 0
0 10
000
00
0
0000
NA
---shy
-----------shy
(3)
40
44
9 0
1 0
6 0
733
333
0
4365
562
500
5 (7
) 40
60
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 40
eo
0
0 0
0 0
0 10
000
00
0
0000
NA
(3
) 40
4
7
5 0
0 0
8 0
783
333
0
3392
384
600
6 (7
) 40
6
0
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
40
60
0
0 0
0 0
0 10
000
00
0
000
0
NA
-----------shy
(3)
33
37
10
0
0 0
20
0 55
223
9
054
27
33
330
0
7 (7
) 33
6
7
0 0
0 0
0 0
100
0000
000
00
N
A
p (9
) 33
6
7
0 0
0 0
0 0
100
0000
000
00
N
A
0 ------~
(Tab
le 2
Con
td)
gtshy Z
C
)
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Con
diti
ons
~ z
Job
E
i R
i Ii
gt
C
l C
2 C
3 C
4 C
5 C
6 C
7 C
8 S CD
26
40
16
0 1
0 17
0
540
541
0
8209
470
600
CD
0 gt1j
8 (7
) 26
74
0
0 0
0 0
0 10
000
00
0
0000
NA
gt-3 r
(9)
26
74
0 0
0 0
0 0
100
0000
000
00
N
A
ttl
()
36
29
12
0 1
0 22
0
453
125
0
7050
342
900
0 0
9
(7)
36
64
0 0
0 0
0 0
100
0000
000
00
N
A
CD
t
(9)
36
64
0 0
0 0
0 0
100
0000
000
00
N
A
0 ~
0 0(3
) 35
29
19
0
0 0
17
0 44
615
4
079
33
52
780
0
gt-3 r10
(7
) 35
65
0
0 0
0 0
0 10
000
00
0
0000
NA
s
(9
) 35
65
0
0 0
0 0
0 10
000
00
0
0000
NA
(3
) 44
12
20
0
0 0
24
0 21
428
6
054
37
45
450
0
15
(7)
44
56
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
44
56
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
42
9 27
0
0 0
22
0 15
517
2
113
59
55
100
0
20
(7)
42
58
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
42
58
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
53
6 19
0
0 0
22
0 12
766
0
050
21
46
340
0
30
(7)
53
47
0 0
0 0
0 0
100
0000
000
00
N
A
53
47
0 ()
0 0
0 0
100
0000
000
00
N
A
CJ
) C
J)
Of
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w
Job
--~~
~~-
~--
Ri
Ii
w
CIl
Cl
C2
C
3 C
4
C5
C6
C7
C
8 -----------shy
(3)
51
3 21
0
1 0
24
0
61
22
4
0
46
39
456
500
40
(7
) 51
4
9
0 0
0 0
0 0
10
00
00
0
0
00
00
NA
(9
) 51
4
9
0 -----_
_
-shy
0 0
0 --shy-----~~~~
0 0
-----------shy
10
00
00
0
0
00
00
---------shy
NA
(3
) 4
8
4 33
0
0 0
15
0
769
23
0
62
20
68
75
00
50
(7
) 4
8
52
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
48
5
2
0 0
0 0
0 0
100
0000
00
00
0
N
A
51
4 3
7
0 0
0 8
0 8
16
33
05
05
8
8
22
20
0
10
0
(7)
51
49
0
0 0
0 0
0 10
000
00
0
00
00
NA
(9
) 51
4
9
0 0
0 0
0 0
100
0000
00
00
0
N
A
----------shy
(3)
56
0
35
0 0
0 9
0 0
00
00
02
29
1
79
550
0
20
0
(7)
56
4
4
0 0
0 0
0 0
100
0000
00
00
0
N
A
(9)
56
4
4
0 0
0 0
0 0
100
0000
00
00
0
N
A
69
2
24
0
0 0
5 0
64
51
6
0
13
97
82
76
00
30
0
(7)
69
31
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
(9)
69
31
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
(3)
62
1
35
0
0 0
2 0
26
31
6
0
17
96
94
59
00
~
40
0
(7)
62
3
8
0 0
0 0
0 0
10
00
00
0
0
00
00
NA
0
(9)
62
3
8
0 0
0 0
0 0
100
0000
00
00
0
N
A
fja
tJte
2-C
onat
)
gt-3
)shy z Cl
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0gtshy
Con
diti
ons
Job
E
i R
i Ii
C
l C
2 C
3 C
4 C
5 C
6 C
7 C
8 ~ rt
J gt-
lt57
0
38
0 0
0 5
0 0
0000
015
97
88
370
0
rtJ
j
500
(7)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
0 3
(9)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
tr1
tlj
0(3
) 57
1
39
0 0
0 3
0 2
3256
014
97
92
860
0
0 rtJ
60
0
(7)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
~ 57
43
0
0 0
0 0
0 10
000
00
0
0000
NA
0 0
1 34
0
gt-lt
(3)
64
0 0
1 0
277
78
0
1198
971
400
tO
3
700
(7)
64
36
0 0
0 0
0 0
100
0000
000
00
N
A
tr1 ~
(9)
64
36
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
60
2 38
0
0 0
0 0
500
00
0
0999
100
0000
800
(7)
60
40
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
60
40
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
62
0 38
0
0 0
0 0
000
00
0
0999
100
0000
900
(7)
62
38
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
62
38
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
60
0 39
0
0 0
1 0
000
0
010
99
97
500
0
1000
(7
) 60
40
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 60
40
0
0 0
0 0
0 10
000
00
0
0000
NA
w
w
(T
able
2 C
ontd
)
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------
CIJ
Con
diti
ons
CIJ
Job
E
i R
i Ii
O
J
C1
C2
C3
C4
C
5 C
6 C
7
C8
Min
44
615
4
000
00
0
0000
Sm
all
(3
) M
ax
951
220
0
8209
562
500
pro
ble
ms
Avg
66
121
6
050
01
39
021
3
_
Min
0
0000
009
99
45
450
0
Lar
ge
(3
) M
ax
214
286
1
1359
100
0000
p
rob
lem
s A
vg
605
84
0
3374
784
187
--
-_
__
---
Min
0
0000
000
00
0
0000
Ov
eral
l
(3)
Max
95
122
0
113
59
10
000
00
pro
ble
ms
Avg
26
950
0
039
40
64
715
2
tr1
()
gt-3 ~ Q
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gtshyT
able
3
z C
om
par
ativ
e re
sult
s fo
r th
e C
GP
S
CD
S
an
d N
EH
alg
ori
thm
s ~
Job 3
4
5
6
7
8
9
10
15
20
30
40
50
100
200
101
39
101
48
102
64
102
82
102
62
102
7
102
82
102
55
102
43
101
83
101
06
100
85
100
65
100
32
100
17
~CDS
100
96
101
28
102
14
102
32
102
46
102
15
102
04
102
04
101
78
101
53
100
99
100
63
100
55
100
35
100
17
~~--------------
~NElI
100
1
100
39
100
53
100
54
100
48
100
72
100
50
100
47
100
25
100
33
100
08
100
05
100
06
100
02
100
01
(Tab
le 3
Con
td)
lfl
gt-
lfl
0 tJ r t=J
0 0 i
lfl gtshy r 0 0 ~ r $
w
w
to
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-----
---
co
jgt
~CGPS
~CDS
~NE
H 0
300
100
07
100
11
100
00
400
100
04
100
06
100
00
500
100
02
100
06
100
00
600
100
02
100
03
100
00
700
100
01
100
02
100
00
800
100
02
100
02
100
00
900
100
01
100
04
100
00
1000
1
0001
1
0003
1
0000
Min
1
0139
1
0096
1
0010
Sm
all
pro
ble
ms
Max
1
0282
1
0246
1
0072
Avg
1
0238
1
0192
1
0047
Min
1
0001
1
0002
1
0000
Lar
ge
pro
ble
ms
Max
1
0243
1
0178
1
0033
Avg
1
0050
1
0042
1
0005
Min
1
0001
1
0002
1
0000
r
Ov
eral
l p
rob
lem
s M
ax
102
82
102
46
100
72
0 -3A
vg
101
15
100
95
100
20
p Z
(Tab
le 3
Con
td)
0
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lgtT
able
3 (
Con
td)
Z
--------
_shy
SCG
PS
EQ
CG
PS
SCD
S SC
GP
S E
QC
GP
S SN
EH
SN
EH
EQ
IjE
H
SCD
S --
~ Jo
b C
DS
CDS
CG
PS
NEH
N
EH
CG
PS
CD
S CD
S N
EH
en
en
3 0
91
9
0
83
17
12
88
0
0 gtzj
4 2
8
7
11
2
70
28
21
76
3
gt-l
[1j
5 2
82
16
4
58
38
28
66
6
0 0 1
j6
3
77
20
1
51
48
40
56
4
en
7 5
75
20
5
43
52
45
50
5
~ 0 0
8 6
70
24
8
35
57
44
47
9
~
gt-l
9 5
6
7
28
2
40
58
46
46
8
10
7
68
25
5
40
55
42
50
8
15
7
70
23
2
45
53
46
50
4
20
8
65
27
4
46
50
37
58
5
30
8
74
18
0
57
43
35
63
2
40
7
72
21
3
53
44
29
62
9
50
13
62
25
4
49
47
31
59
10
10
0
15
6
0
25
1
52
47
27
71
2
20
0
17
64
19
2
55
43
26
68
6
30
0
12
74
14
7
63
30
17
74
9
w
(T
able
3 C
ontd
)
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Job
S
co
Ps
CD
S E
QC
GP
SC
DS
SCD
S C
GPS
SC
GP
S N
EH
E
QC
GP
SN
EH
S
NE
H
CG
PS
NE
H
SCD
S E
Q
EH
CD
S SC
DS
NE
Il
N
400
14
63
23
5
58
37
14
81
5
500
13
62
25
1
57
42
20
78
2
60
0
9
61
30
4
54
42
12
82
6
700
11
66
23
8
56
36
13
79
8
80
0
10
63
27
4
57
39
14
82
4
900
16
63
21
2
60
38
17
81
2
1000
11
61
28
4
57
39
12
82
6
Sm
all
pro
ble
ms
Min
Max
Avg
0
67
9
7
91
28
375
00
77
125
0
191
250
0
8
337
50
35
17
83
58
525
000
44
1250
12
46
347
500
46
88
598
750
0
9
537
50
Lar
ge
pro
ble
ms
Min
Max
7
17
60
74
14
30
0
8
45
63
30
53
12
46
50
82
2
10
114
00
65
3333
2
326
67
3
4000
546
000
42
0000
233
333
71
333
3
533
33
Ov
eral
l p
rob
lem
s
Min
Max
Avg
0
60
9
17
91
30
873
91
69
434
8 2
182
61
0
8
339
13
35
17
83
58
538
696
42
739
1
12
46
273
043
~~~--~--
46
0
88
10
673
478
5
3478
c 0 ~ gtshy L
0
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343 ANALYSIS OF THE CGPS ALGORITHM
average-case performance ratio the NEH algorithm is the best one folshylowed by the CDS algorithm and then by the CGPS algorithm As comshypared with the CDS algorithm in terms of the proportional superiority 87391 of problem instances better by the CGPS algorithm 694348 of problem instances equal by the CGPS and CDS algorithms and 218261 of problem instances better by the CDS algorithm on the average It follows that the CDS algorithm is better than the CGPS alshygorithm especially for the large problems Similarly the NEH algorithm is better than the CGPS algorithm and the NEH algorithm is better than the CDS algorithm especially for the small problems Therefore we have the same conclusion about these heuristics in terms of the empirishycal average-case performance ratio and proportional superiority
To sum up 503478 ofthe initial permutation schedules generated by the CGPS algorithm are the optimal schedules Among the 496522 of non-optimal schedules 647154 of the permutation schedules 1t2 improve the makespan of the initial permutation schedule However the improvement made by 1t2 is only 03940 over 1tl in terms of the empirical average-case performance ratio Moreover compared with the NEH and CDS algorithms the CGPS algorithm is the worst one in terms of the empirical average-case performance ratio and proportional superiority These imply that the CGPS algorithm is not good enough for applications
5 CONCLUSION
In this paper the 3FSS with minimum makespan criterion is considered Theoretically the heuristic algorithm proposed by Chen et a1 [2] has the best worst-case performance ratio of 53 This paper evaluates the performance of the CGPS algorithm from the empirical perspective Three simulation experiments are performed to analyze its performance in terms of the six evaluation measures on a total of 13800 problems Four conclusions can be drawn from this paper Firstly in the CGPS algorithm 503478 of the initial permutation schedules are the optimal ones 321326 of the permutation schedshyules 1t2 improve the makespan of nl and 175196 of the permutation schedules n2 do not improve their makespan Secondly the empirical average-case performance ratio of initial permutation schedule genershyated by the CGPS algorithm is always smaller than the performance guarantee 53 developed by Chen et al [2] Its empirical average-case performance ratio is in the range of 10011 and 10355 and is 10156 on the average Thirdly the permutation schedule 1t2 improves 1tl in terms of the empirical average-case performance ratio by the non-significant percentage 03940 Fourthly among the three heuristic algorithms the
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344 H C TANG
NEH algorithm is the best one in terms of both empirical average-case performance ratio and proportional superiority followed by the CDS alshygorithm and then by the CGPS algorithm Consequently the CGPS algoshyrithm is not suitable for the 3FSS with minimum makespan criterion
REFERENCES
l H G Campbell R A Dudek and M L Smith A heuristic algorithm for the n-job tn-machine sequencing problem Management Science Vol 16 (1970) pp 630-637
2 B Chen C A Glass C N Potts and V A Strusevich A new heuristic for threeshymachine flow shop scheduling Operations Research Vol 44 (1996) pp 891-898
3 R W Conway W L Maxwell and L W Miller Theory of Scheduling AddisonshyWesley Reading MA 1967
4 R A Dudek S S Panwalkar and M L Smith The lessons of flowshop scheduling research Operations Research Vol 40 (1992) pp 7-13
5 M R Garey D S Johnson and R Sethi The complexity of flowshop and jobshop scheduling Mathematics Operations Research Vol 1 (1976) pp 117-129
6 S M Johnson Optimal two- and three-stage production schedules with setup times included Naval Research Logistic Quarterly Vol 1 (1954) pp 61-68
7 B J Lageweg J K Lenstra and A H G Rinnooy Kan A general bounding scheme for the permutation flow-shop problem Operations Research Vol 26 (1978) pp 53-67
8 E L Lawler J K Lenstra A H G Rinnooy Kan and D B Shmoys Sequencing and Scheduling Algorithms and Complexity in S C Graves A H G Rinnooy
-Kan and P H Zipkin Handbooks in Operations Research and Management Science Vol 4 Logistics of Production and Inventory North Holland Amsterdam 1993 pp 445-522
9 J K Lenstra A H G Rinnooy Kan and P Brucker Complexity of machine scheduling problems Annals Discrete Mathematics Vol 1 (1977) pp 343-362
10 M Nawaz E E Enscore Jr and I Ham A heuristic algorithm for the m-machine n-job flow-shop sequencing problem OMEGA Vol 11 (1983) pp 91-95
II C N Potts An Adaptive branching rule for the permutation flow-shop problem European Journal of Operational Research Vol 5 (1980) pp 19-25
12 A H G Rinnooy Kan Machine scheduling problems classification complexity and computations Nijhoff The Hague 1976
13 H Rock and G Schmidt Machine aggregation heuristics in shop scheduling Methods of Operations Research Vol 45 (1983) pp 303-314
14 C Smutnicki Some results of the worst-case analysis for flow shop scheduling European Journal of Operational Research Vol 109 (1998) pp 66-87
15 S M A Suliman A two-phase heuristic approach to the permutation flow-shop scheduling problem International Journal of Production Economics Vol 64 (2000) pp 143-152
16 H C Tang A new lower bonding rule for permutation flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 249-257
17 H C Tang A modification to the CGPS algorithm for three-machine flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 321-331
Received January 2001
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324 H C TANG
optimal schedule that is a permutation schedule viz schedule with the same processing order on each machine [3 12] Another property is that the 3FSS with minimum makespancriterion is a strongly NPshyhard problem [5 9] while the two-machine FSS with minimum makespan criterion has a computational time complexity of O(n log n) [6J Therefore much research has been devoted to proposing heuristic algorithms yielding near-optimal schedules in a quick time Two heuristic algorithms are distinguished constructive algorithms and improvement algorithms For more details see Smutnicki [14] Sullman [15] and the references cited there In this paper we restrict overselves to the constructive algorithms for the 3FSS with minimum makespan criterion
Moreover the measures of a heuristic algorithm performance can be investigated either experimentally or analytically (worst-case analysis or probability analysis) As the worst-case analysis is concerned the heuristic algorithm developed by Chen Glass Potts and Strusevich (CGPS) [2J is the best one so far This is very inspiring However no experimental analysis of its performance has been reported to date
The primary purpose of this paper is to measure the CGPS alshygorithm performance by the experimental analysis In the following sections we first recall the CGPS algorithm for the 3FSS with minimum makespan criterion A number of evaluation measures are proposed in section 3 Detailed evaluations and comparisons are performed in section 4 Finally some concluding remarks are made
2 THE CGPS ALGORITHM
The CGPS algorithm is an O(n log n) time heuristic algorithm which has a worst-case performance ratio of 53 for the 3FSS with minimum makespan criterion There are two main stages for the CGPS algorithm Firstly the construction of an initial permutation schedule 1tl involves creating an artificial two-machine FSS problem by the machine aggregation heuristic of ROck and Schmidt [13] and then applying Johnsons algorithm [6] to obtain 1tl The second stage is attempting to improve 1tl by partitioning the middle jobs into subshyjects and then rescheduling the jobs The resulting permutation schedule is 1t2 Of the two perm utation schedules 1tl and 1t2 the CGPS algorithm is shown that at least one of these permutation schedules has makespan which is at most 53 times that of an optimal schedule
For notational convenience we adopt the same notations as Chen et al [2] Let ai hi and ci be the uninterrupted processing times of job i E N I 2 n on machines A Band C respectively Also let 1t
0 deshynote an optimal permutation schedule that minimizes the makespan
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325 ANALYSIS OF THE CGPS ALGORITHM
For N ~ N we define a(N) =L ai The notation B(N) and c(N) are iEN
defined similarly For the 1tl obtained by applying Johnsons algorithm [6] we assume that Il and v are critical jobs where 1 S Il S v s n We also assume that
Nl = I 2 Il- I Nz = Il + 1 v - I and
N3 =v+ 1 n
Then the makespan of 1tl is rewritten as
a(Nl) + aJ1 + bfl + cJ1 + c(Ns) if Il v Cmax(n l ) (1)
ja(Nl ) + aJ1 + bJ1 + b(N2) + bv + Cv + c(Ns) if Il v
In the following we present two lemmas that are useful in the subsequent analysis
V It
LEMMA 1 Cmain) ~ L ak + bv + L Ck for v == 1 2 n (2) k=l k=y
PROOF See p 893 in Chen et a1 [2] 0
Next Chen et a1 [2] applied one value of v in (2) to establish conditions under which the schedule 1tl has a worst-case performance ratio of 53
LEMMA 2 If Il and v are critical jobs in 1tgt then
(a) If Il == v then 1tl is an optimal permutation schedule (b) If Il lt v and
beNz) + minbJ1 by S 23 beN) (3)
then we have Cmax(1tl)Cmax(1t) S 53 (4)
PROOF See p 893 11 Chen et a1 [2] 0
If Il lt v and (3) is violated Chen et a1 [2] constructed a schedule 1t2 such that the better of 1tl and 1t2 has a worst-case performance ratio of 53
LEMMA 3 If Il lt v (3) is violated a(N1) + aJ1 ~ Cy + c(N3) and (nl)Cmax (1t) ~ 53 then the permutation schedule 1tz generatedCmax
by the CGPS algorithm satisfies
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326 HCTANG
Cmax(1ti)ICmax(1tmiddot) ~ 53 (5)
PROOF See p 895 in Chen et al [2) 0
Two notable observations are obtained from the CGPS algorithm One is that the desired performance (4) sometimes holds but (3) violates The other is that the schedule 1t2 does not necessarily represent an imshyprovement on 1t1 in its makespan Detailed descriptions are presented in the following
We now concern the necessary and sufficient conditions for the desired performance (4) In Lemma 2 we can recognize that condition (3) is a sufficient for the desired performance (4) However as Chen et al [2) point out (4) sometimes holds also when (3) is violated since the lower bound used in Lemma 2 is not sufficient sharp In other words (3) is a sufficient but not necessary condition for (4) Thus we propose a modification to the CGPS algorithm by changing condition (3) While we derive new lower bounds we shall be concerned with the trade-off between the sharpness of lower bounds and their computashytional requirements As indicated in Chen et al [2] the lower bounds used in the analysis are not sufficient strong Then we shall propose some stronger lower bounds with a little extra computational requirement One way improving upon lower bound of the optimal schedule is to apply two values of v in (2)
LEMMA 4 If fl and v are critical jobs in 1t1 and fl lt v then
(a) Cmai1tmiddot) ~ plusmnak + i Ck + max b~ + ~ Ck plusmnak + bv ) (6) kl kv k=1l k=~+l
(b) We can replace (3) by
b(Ni) + bll + bv - maX[bll + ~ Ck plusmnak + b) ~ 23 beN) (7) k=~ k=IHl
(c) If condition (3) holds then (7) holds
PROOF After applying (2) with v fl and v v gives
Inequality (6) is now derived
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327 ANALYSIS OF THE CGPS ALGORITHM
To prove part (b) we subtract (6) from (1) to obtain
Cmai 1t l) - Cmai 1tmiddot) $ a(N1) + all + bit + b(Nz + by + cy+ c(Na)
Substituting (7) yields
Cmai1tl) - Cmai1tmiddot)$ 23 b(N)
The desired inequality (4) is now deduced from C (1t) b(N) Somax that we can replace (3) by (7)
Finally since
b(N2) + bit + bv - maxi bit + ~ Ck I ak + by ) w1t kdegIt+1
$ b(N2) + bit + by - max blL by)
== b(N2) + min bit by
it follows that if (3) holds then (7) holds This completes the proof of the lemma 0
Another approach for deriving a sharper lower bound of the optimal schedule is using (2) From the proof of Lemma 1 in Chem et al [2] the lower bound of an optimal schedule is
(8)
Iy n )where max L (Jk + L Pk 11 $ v $ n is the makespan of the artificial
k=l k=y
two-machine FSS with processing times Ctk == ak + bk and Pk = bk + Ck
for kEN It follows that condition (3) can be replaced by
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328 H C TANG
It is recognized that the lower bound (8) considers all values of v in (2) but the lower bound (6) only considers two of its values This implies that the lower bound (8) is stronger than (6) Therefore condition (9) is less restrictive than condition (7) This result is stated formally below
LEMMA 5 If condition (7) holds then (9) holds
Therefore among the three conditions condition (9) is the least restrictive from Lemma 4 and Lemma 5
The second notable observation is that the schedule 1t2 does not always improve the makespan of the schedule 1tl In other words the makespan of1t2 sometimes is larger than or equal to the makespan Of1tl
Taking into consideration the above pieces of information our conclusion is that eight cases arise through the various possible comshybinations For condition (3) ~s an example these cases are
(C1) 11 =v
(C2) 11 lt v and (3) holds
(C3) 11 lt v (3) is violated Cmai1tl) gt Cmai1t0 and (4) holds
(C4) 11 lt v (3) is violated Cmai1tl) gt Cmai1t2) and (4) is violated (10)
(C5) 11 lt v (3) is violated Cmai1tl) = Cmai1t2) and (4) holds
(C6) 11 lt v (3) is violated Cmai1tl) = Cmai1t0 and (4) is violated
(C7) 11 lt v (3) is violated Cmai1tl) lt Cmai1t2) and (4) holds
(C8) 11 lt v (3) is violated Cmai1tl) lt Cmai1t2) and (4) is violated
Similarly for other two proposed conditions we replace (3) by (7) and (9) respectively
3 EVALUATION MEASURES
To evaluate the performance of the CGPS algorithm this paper provides a number of evaluation measures in three respects The first respect refers to the effectiveness of the CGPS algorithm The second respect is concerned with the comparison of the three conditions satisfyshying the desired performance (4) The final respect is apout comparing with the heuristics developed by Nawaz Enscore and Ham (NEH) [10] and by Campbell Dudek and Smith (CDS) [1] which are the best two among the constructive algorithms for the FSS with minimum makespan criterion
Firstly to measure the effectiveness of the CGPS algorithm two evaluation measures are adopted In Lemma 2 for the case that
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329 ANALYSIS OF THE CGPS ALGORITHM
l == v an optimal schedule is found from the CGPS algorithm It follows that the percentage of schedules that are equal to the optimal ones is used to measure its effectiveness This proportional optimum is defined as
P [(C1)Tl 100 (11)
where (C1) denotes the number of times the schedule Xl that is equal to an optimal one in T problem instances Another evaluation measshyure is the empirical average-case performance ratio For the algorithm A and problem instance Z its quality is measured by the ratio
(12)
It is recognized that the worst-case performance ratio provides a performance guarantee which is valid for all problem instances As stated in Chen et al [2] the CGPS algorithm is by far the best one with the worst-case performance ratio as the performance measure However the Operations Research practitioners also concern the comshyputational experience of the CGPS algorithm Thus we adopt the empirical average-case performance ratio
T
~A L llA(Zi)T (13) i=l
to measure its performance empirically For notational convenience the argument Z will be omitted in Cmaix Z) and Cmax(x Z) if it is not necessary in the following
Secondly to compare conditions (3) (7) and (9) satisfying the deshysired performance (4) three evaluation measures are proposed We only consider condition (3) since the other conditions are treated simishylarly It is recognized that the desired performance (4) sometimes holds but condition (3) violates This implies that the percentage of equivalent relation between condition (3) and the desired performance (4) can be used as an evaluation measure This proportional equivashylency can be defined as
i _[ (C2) + (C4) +(C6) + (C8) ] E - (C2)+(C3)+(C4)+(C5)+(C6)+(C7)+(C8) 100
for i == 3 7 9 (14)
where (C2) + (C4) + (C6) + (C8) is thee number of times the equivalent relation between the desired performance (4) and condition (3) The other evaluation measures concern an improvement on the makespan of the schedule Xl Two evaluation measures are distinshy
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330 HCTANG
guished One is the relative error of the schedule 1tl from the schedule 1tH in terms of the 11Z)
i [ Cmax(1tH Z)Cmax(1t Z) 1R 1- 100 Cmax(1tl Z)CmaxC1t Z)
Cmax(1tH Z) ] (15) 1 - C ( l Z) 100 for pound 3 7 9[ 1tmax
The other is an improvement on the makespan of the schedule 1t1 in terms of the problem instance The definition of this proportional imshyprovement is
Ii _ [ (C3) + (C4) ] - (C3) +(C4) +(C5) +(C6) +(C7) +(C8) 100
for i 3 7 9 (16)
where (C3) + (C4) is the number of times the schedule 1tH that imshyproves the makespan of the schedule 1tl
Finally in concern with the comparison of the CGPS NEH and CDS algorithms two evaluation measures are adopted One measure is based on the empirical average-case performance ratio (13) The other measure is that the percentage of problem instances that the A algorithm is superior to the B algorithm in its makespan This proporshytional superiority can be defined as
A _[ (A) ] (17)SB - (A) +(AB) + (B) 100
where (A) and (B) are the number of times the makespan better by A and B algorithms respectively and (AB) is the number of times the makespan equal by A and B algorithms Denote E~ the percentage of problem instances that the A algorithm is equal to the B algorithm
Therefore there are six evaluation measures adopted in this paper We use (11) and (13) to measure the effectiveness of the CGPS algoshyrithm To compare conditions (3) (7) and (9) satisfying the desired pershyformance (4) three evaluation measures (14) (15) and (16) are adopted To compare the CGPS algorithm with the NEH algorithm as well as the CDS algorithm two evaluation measures (13) and (17) are used
4 SIMULATION EXPERIMENTS
To get an idea of the CGPS algorithm from the empirical perspecshytive three simulation experiments are conducted in this paper The first simulation experiment examines the effectiveness of the CGPS algoshy
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331 ANALYSIS OF THE CGPS ALGORITHM
rithm The second simulation experiment concerns the empirical comparishyson of conditions (3) (7) and (9) which are the sufficient for the desired performance (4) The third simulation experiment conducts the comparison of the CGPS algorithm with the NEH and CDS algorithms
The number of machines considered is three and the numbers of jobs considered are 3 4 5 6 7 8 9 10 15 20 30 40 50 100 200 300 400 500 600 700 800 900 and 1000 The problem size is dishyvided into two classes One is the small problem for the 3 4 5 6 7 8 9 and 10 job problems The other is the large problem for the 15 20 30 40 50 100 200 300 400 500 600 700 800 900 and 1000 job problems One hundred problems are generated for each job The processing times for each problem are randomly generated integers uniformly distributed between 1 and 100 A more portable random number generator Xn == 16807xH(mod 231
- 1) is adopted using same to generate the random values for each problem The whole simushy
lation experiments are subjected to extensive testing 13800 problems A number of evaluation measures described in previous section
are used for the three simulation experiments We adopt the evaluation measures (11) and (13) for the first simulation experiment (14) (15) and (16) for the second simulation experiment (13) and (17) for the third simulation experiment respectively The whole computation is conducted on a 266 MHz Pentinum II PC using Microsoft Visual C++ compiler under Microsoft Windows 98 operating system
To obtain an optimal schedule for the empirical average-case pershyformance ratio the branch-and-bound algorithm with two-machine lower bound rule proposed by Tang [16] is applied This proposed twoshymachine lower bound is a modification to the algorithms of Lageweg et al [7] and Potts [11] and is shown to be more efficient Whenever a problem is not solved after 100000 nodes have been generated comshyputation is stopped In this case the optimal makespan is replaced by the best-known makespan Thus the empirical average-case pershyformance ratio is underestimated
The first simulation experiment is concerned with the effectiveness of the CGPS algorithm Detailed results are presented in Table 1 where Min denotes the minimum value of proportional optimum or empirical average-case performance ratio Max and Avg the corresshyponding maximum and average values respectively The proportional optimum is 503478 on the average In other words 503478 of the initial permutation schedules generated by the CGPS algorithm are the optimal schedules especially for the large problems the empirical average-case performance ratio is concerned Table 1 lists the percentage POP of problem instances that the optimal schedules are found by the
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332 H C TANG
branch-and-bound algorithm with Tangs two-machine lower bound rule [16] The average percentage Popt is 962609 Especially in the case of small problems the branch-and-bound algorithm can always find the optimal schedules within 100000 nodes generated Therefore it is appropriate to adopt the empirical average-ca~e performance ratio to measure the effectiveness of the CGPS algorithm The empirical average-case performance ratio of the initial permutation schedule is in the range of 10011 and 10355 and is 10156 on the average While the empirical average-case performance ratio of the CGPS algorithm is in the range of 10001 and 10282 and is 10115 on the average These values are always smaller than the performance guarantee 53 of the CGPS algorithm
Table 1 Results of simulations for the effectiveness of the COPS
Job P Popt ~1t1
3 59 100 10139 4 53 100 10185 10148 5 40 100 10309 10264 6 40 100 10317 10282 7 33 100 10318 10262 8 26 100 10355 10270 9 36 100 10355 10282 10 35 100 10337 10255 15 44 84 10299 10243 20 42 82 10300 10183 30 53 90 10157 10106 40 51 94 10132 10085 50 48 95 10128 10065 100 51 93 10083 10032 200 56 97 10040 10017 300 69 99 10021 10007 400 62 98 10022 10004 500 57 96 10018 10002 600 57 99 10017 10002 700 64 96 10013 10001 800 60 99 10012 10002
(Table 1 Contd)
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333 ANALYSIS OF THE CGPS ALGORITHM
lOOO 60 97 10001
Small problems Max
Avg 59
402500 lOO lOO
10355 10289
10139 10282 10238
Large problems
Min Max
42 82 10011 10300
10001 10243 10050
Overall problems Max 69
503478 100
922609 10355 10156
10001 10282 10115
The second experiment refers to the comparison of conditions (3) (7) and (9) in terms of the proportional equivalency (14) the relative error (15) and the proportional improvement (16) Table 2 gives the reshysults Notably the number of times of the cases C3 C4 C5 C6 C7 and C8 for conditions (7) and (9) are all zeros In other words the CGPS algorithm with condition (7) or (9) only generates the initial permutashytion schedule It follows that the proportional equivalency is lOO the relative error is zero and the proportional improvement is not availshyable (NA) for each problem These results indicate that the initial pershymutation schedule never violates the worst-case performance ratio of 53 if we adopt the CGPS algorithm with condition (7) or (9)
In the case of condition (3) viz the original CGPS algorithm the proportional equivalency is 2695 on the average This value indicates that 7305 of the desired performance (4) holds also when condition (3) is violated Therefore condition (3) is too restrictive especially for the large problems In concern with the improvement on the makesshypan of 1tl the relative error is 03940 and the proportional improveshyment is 647152 on the average This implies that 647154 of the permutation schedule 1t2 has improved on the initial permutation schedule in its makespan However the quality of improvement made by 1t2 over 1tl is only 03940 in terms of the empirical average-case performance ratio Therefore condition (3) in the CGPS algorithm is more restrictive and the improvement in the average-case performance ratio is so marginal from the empirical point of view
The third experiment concerns the comparison of the CGPS algoshyrithm with the NEH algorithm as well as the CDS algorithm Comparashytive results are shown in Table 3 On basis of the empirical
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ltgtl
ltgtl
Tab
le 2
C
om
para
tiv
e r
esu
lts
for
co
nd
itio
ns
(3)
(7)
an
d (
9)
--------------shy
Job
C
ondi
tion
s E
i R
i Ii
C
1 C
2 C
3 C
4 C
5 C
6 C
7 C
8 -----shy
(3)
59
39
0 0
2 0
0 0
951
220
0
0000
000
00
3 (7
) 59
41
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 59
41
0
0 0
0 0
0 10
000
00
0
0000
NA
-shy
(3)
53
39
4 0
0 0
4 0
829
787
0
3633
500
000
4 (7
) 53
47
0
0 0
0 0
0 10
000
00
0
0000
NA
53
47
0
0 0
0 0
0 10
000
00
0
0000
NA
---shy
-----------shy
(3)
40
44
9 0
1 0
6 0
733
333
0
4365
562
500
5 (7
) 40
60
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 40
eo
0
0 0
0 0
0 10
000
00
0
0000
NA
(3
) 40
4
7
5 0
0 0
8 0
783
333
0
3392
384
600
6 (7
) 40
6
0
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
40
60
0
0 0
0 0
0 10
000
00
0
000
0
NA
-----------shy
(3)
33
37
10
0
0 0
20
0 55
223
9
054
27
33
330
0
7 (7
) 33
6
7
0 0
0 0
0 0
100
0000
000
00
N
A
p (9
) 33
6
7
0 0
0 0
0 0
100
0000
000
00
N
A
0 ------~
(Tab
le 2
Con
td)
gtshy Z
C
)
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Con
diti
ons
~ z
Job
E
i R
i Ii
gt
C
l C
2 C
3 C
4 C
5 C
6 C
7 C
8 S CD
26
40
16
0 1
0 17
0
540
541
0
8209
470
600
CD
0 gt1j
8 (7
) 26
74
0
0 0
0 0
0 10
000
00
0
0000
NA
gt-3 r
(9)
26
74
0 0
0 0
0 0
100
0000
000
00
N
A
ttl
()
36
29
12
0 1
0 22
0
453
125
0
7050
342
900
0 0
9
(7)
36
64
0 0
0 0
0 0
100
0000
000
00
N
A
CD
t
(9)
36
64
0 0
0 0
0 0
100
0000
000
00
N
A
0 ~
0 0(3
) 35
29
19
0
0 0
17
0 44
615
4
079
33
52
780
0
gt-3 r10
(7
) 35
65
0
0 0
0 0
0 10
000
00
0
0000
NA
s
(9
) 35
65
0
0 0
0 0
0 10
000
00
0
0000
NA
(3
) 44
12
20
0
0 0
24
0 21
428
6
054
37
45
450
0
15
(7)
44
56
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
44
56
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
42
9 27
0
0 0
22
0 15
517
2
113
59
55
100
0
20
(7)
42
58
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
42
58
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
53
6 19
0
0 0
22
0 12
766
0
050
21
46
340
0
30
(7)
53
47
0 0
0 0
0 0
100
0000
000
00
N
A
53
47
0 ()
0 0
0 0
100
0000
000
00
N
A
CJ
) C
J)
Of
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w
Job
--~~
~~-
~--
Ri
Ii
w
CIl
Cl
C2
C
3 C
4
C5
C6
C7
C
8 -----------shy
(3)
51
3 21
0
1 0
24
0
61
22
4
0
46
39
456
500
40
(7
) 51
4
9
0 0
0 0
0 0
10
00
00
0
0
00
00
NA
(9
) 51
4
9
0 -----_
_
-shy
0 0
0 --shy-----~~~~
0 0
-----------shy
10
00
00
0
0
00
00
---------shy
NA
(3
) 4
8
4 33
0
0 0
15
0
769
23
0
62
20
68
75
00
50
(7
) 4
8
52
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
48
5
2
0 0
0 0
0 0
100
0000
00
00
0
N
A
51
4 3
7
0 0
0 8
0 8
16
33
05
05
8
8
22
20
0
10
0
(7)
51
49
0
0 0
0 0
0 10
000
00
0
00
00
NA
(9
) 51
4
9
0 0
0 0
0 0
100
0000
00
00
0
N
A
----------shy
(3)
56
0
35
0 0
0 9
0 0
00
00
02
29
1
79
550
0
20
0
(7)
56
4
4
0 0
0 0
0 0
100
0000
00
00
0
N
A
(9)
56
4
4
0 0
0 0
0 0
100
0000
00
00
0
N
A
69
2
24
0
0 0
5 0
64
51
6
0
13
97
82
76
00
30
0
(7)
69
31
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
(9)
69
31
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
(3)
62
1
35
0
0 0
2 0
26
31
6
0
17
96
94
59
00
~
40
0
(7)
62
3
8
0 0
0 0
0 0
10
00
00
0
0
00
00
NA
0
(9)
62
3
8
0 0
0 0
0 0
100
0000
00
00
0
N
A
fja
tJte
2-C
onat
)
gt-3
)shy z Cl
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0gtshy
Con
diti
ons
Job
E
i R
i Ii
C
l C
2 C
3 C
4 C
5 C
6 C
7 C
8 ~ rt
J gt-
lt57
0
38
0 0
0 5
0 0
0000
015
97
88
370
0
rtJ
j
500
(7)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
0 3
(9)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
tr1
tlj
0(3
) 57
1
39
0 0
0 3
0 2
3256
014
97
92
860
0
0 rtJ
60
0
(7)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
~ 57
43
0
0 0
0 0
0 10
000
00
0
0000
NA
0 0
1 34
0
gt-lt
(3)
64
0 0
1 0
277
78
0
1198
971
400
tO
3
700
(7)
64
36
0 0
0 0
0 0
100
0000
000
00
N
A
tr1 ~
(9)
64
36
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
60
2 38
0
0 0
0 0
500
00
0
0999
100
0000
800
(7)
60
40
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
60
40
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
62
0 38
0
0 0
0 0
000
00
0
0999
100
0000
900
(7)
62
38
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
62
38
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
60
0 39
0
0 0
1 0
000
0
010
99
97
500
0
1000
(7
) 60
40
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 60
40
0
0 0
0 0
0 10
000
00
0
0000
NA
w
w
(T
able
2 C
ontd
)
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------
CIJ
Con
diti
ons
CIJ
Job
E
i R
i Ii
O
J
C1
C2
C3
C4
C
5 C
6 C
7
C8
Min
44
615
4
000
00
0
0000
Sm
all
(3
) M
ax
951
220
0
8209
562
500
pro
ble
ms
Avg
66
121
6
050
01
39
021
3
_
Min
0
0000
009
99
45
450
0
Lar
ge
(3
) M
ax
214
286
1
1359
100
0000
p
rob
lem
s A
vg
605
84
0
3374
784
187
--
-_
__
---
Min
0
0000
000
00
0
0000
Ov
eral
l
(3)
Max
95
122
0
113
59
10
000
00
pro
ble
ms
Avg
26
950
0
039
40
64
715
2
tr1
()
gt-3 ~ Q
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gtshyT
able
3
z C
om
par
ativ
e re
sult
s fo
r th
e C
GP
S
CD
S
an
d N
EH
alg
ori
thm
s ~
Job 3
4
5
6
7
8
9
10
15
20
30
40
50
100
200
101
39
101
48
102
64
102
82
102
62
102
7
102
82
102
55
102
43
101
83
101
06
100
85
100
65
100
32
100
17
~CDS
100
96
101
28
102
14
102
32
102
46
102
15
102
04
102
04
101
78
101
53
100
99
100
63
100
55
100
35
100
17
~~--------------
~NElI
100
1
100
39
100
53
100
54
100
48
100
72
100
50
100
47
100
25
100
33
100
08
100
05
100
06
100
02
100
01
(Tab
le 3
Con
td)
lfl
gt-
lfl
0 tJ r t=J
0 0 i
lfl gtshy r 0 0 ~ r $
w
w
to
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-----
---
co
jgt
~CGPS
~CDS
~NE
H 0
300
100
07
100
11
100
00
400
100
04
100
06
100
00
500
100
02
100
06
100
00
600
100
02
100
03
100
00
700
100
01
100
02
100
00
800
100
02
100
02
100
00
900
100
01
100
04
100
00
1000
1
0001
1
0003
1
0000
Min
1
0139
1
0096
1
0010
Sm
all
pro
ble
ms
Max
1
0282
1
0246
1
0072
Avg
1
0238
1
0192
1
0047
Min
1
0001
1
0002
1
0000
Lar
ge
pro
ble
ms
Max
1
0243
1
0178
1
0033
Avg
1
0050
1
0042
1
0005
Min
1
0001
1
0002
1
0000
r
Ov
eral
l p
rob
lem
s M
ax
102
82
102
46
100
72
0 -3A
vg
101
15
100
95
100
20
p Z
(Tab
le 3
Con
td)
0
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lgtT
able
3 (
Con
td)
Z
--------
_shy
SCG
PS
EQ
CG
PS
SCD
S SC
GP
S E
QC
GP
S SN
EH
SN
EH
EQ
IjE
H
SCD
S --
~ Jo
b C
DS
CDS
CG
PS
NEH
N
EH
CG
PS
CD
S CD
S N
EH
en
en
3 0
91
9
0
83
17
12
88
0
0 gtzj
4 2
8
7
11
2
70
28
21
76
3
gt-l
[1j
5 2
82
16
4
58
38
28
66
6
0 0 1
j6
3
77
20
1
51
48
40
56
4
en
7 5
75
20
5
43
52
45
50
5
~ 0 0
8 6
70
24
8
35
57
44
47
9
~
gt-l
9 5
6
7
28
2
40
58
46
46
8
10
7
68
25
5
40
55
42
50
8
15
7
70
23
2
45
53
46
50
4
20
8
65
27
4
46
50
37
58
5
30
8
74
18
0
57
43
35
63
2
40
7
72
21
3
53
44
29
62
9
50
13
62
25
4
49
47
31
59
10
10
0
15
6
0
25
1
52
47
27
71
2
20
0
17
64
19
2
55
43
26
68
6
30
0
12
74
14
7
63
30
17
74
9
w
(T
able
3 C
ontd
)
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Job
S
co
Ps
CD
S E
QC
GP
SC
DS
SCD
S C
GPS
SC
GP
S N
EH
E
QC
GP
SN
EH
S
NE
H
CG
PS
NE
H
SCD
S E
Q
EH
CD
S SC
DS
NE
Il
N
400
14
63
23
5
58
37
14
81
5
500
13
62
25
1
57
42
20
78
2
60
0
9
61
30
4
54
42
12
82
6
700
11
66
23
8
56
36
13
79
8
80
0
10
63
27
4
57
39
14
82
4
900
16
63
21
2
60
38
17
81
2
1000
11
61
28
4
57
39
12
82
6
Sm
all
pro
ble
ms
Min
Max
Avg
0
67
9
7
91
28
375
00
77
125
0
191
250
0
8
337
50
35
17
83
58
525
000
44
1250
12
46
347
500
46
88
598
750
0
9
537
50
Lar
ge
pro
ble
ms
Min
Max
7
17
60
74
14
30
0
8
45
63
30
53
12
46
50
82
2
10
114
00
65
3333
2
326
67
3
4000
546
000
42
0000
233
333
71
333
3
533
33
Ov
eral
l p
rob
lem
s
Min
Max
Avg
0
60
9
17
91
30
873
91
69
434
8 2
182
61
0
8
339
13
35
17
83
58
538
696
42
739
1
12
46
273
043
~~~--~--
46
0
88
10
673
478
5
3478
c 0 ~ gtshy L
0
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343 ANALYSIS OF THE CGPS ALGORITHM
average-case performance ratio the NEH algorithm is the best one folshylowed by the CDS algorithm and then by the CGPS algorithm As comshypared with the CDS algorithm in terms of the proportional superiority 87391 of problem instances better by the CGPS algorithm 694348 of problem instances equal by the CGPS and CDS algorithms and 218261 of problem instances better by the CDS algorithm on the average It follows that the CDS algorithm is better than the CGPS alshygorithm especially for the large problems Similarly the NEH algorithm is better than the CGPS algorithm and the NEH algorithm is better than the CDS algorithm especially for the small problems Therefore we have the same conclusion about these heuristics in terms of the empirishycal average-case performance ratio and proportional superiority
To sum up 503478 ofthe initial permutation schedules generated by the CGPS algorithm are the optimal schedules Among the 496522 of non-optimal schedules 647154 of the permutation schedules 1t2 improve the makespan of the initial permutation schedule However the improvement made by 1t2 is only 03940 over 1tl in terms of the empirical average-case performance ratio Moreover compared with the NEH and CDS algorithms the CGPS algorithm is the worst one in terms of the empirical average-case performance ratio and proportional superiority These imply that the CGPS algorithm is not good enough for applications
5 CONCLUSION
In this paper the 3FSS with minimum makespan criterion is considered Theoretically the heuristic algorithm proposed by Chen et a1 [2] has the best worst-case performance ratio of 53 This paper evaluates the performance of the CGPS algorithm from the empirical perspective Three simulation experiments are performed to analyze its performance in terms of the six evaluation measures on a total of 13800 problems Four conclusions can be drawn from this paper Firstly in the CGPS algorithm 503478 of the initial permutation schedules are the optimal ones 321326 of the permutation schedshyules 1t2 improve the makespan of nl and 175196 of the permutation schedules n2 do not improve their makespan Secondly the empirical average-case performance ratio of initial permutation schedule genershyated by the CGPS algorithm is always smaller than the performance guarantee 53 developed by Chen et al [2] Its empirical average-case performance ratio is in the range of 10011 and 10355 and is 10156 on the average Thirdly the permutation schedule 1t2 improves 1tl in terms of the empirical average-case performance ratio by the non-significant percentage 03940 Fourthly among the three heuristic algorithms the
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344 H C TANG
NEH algorithm is the best one in terms of both empirical average-case performance ratio and proportional superiority followed by the CDS alshygorithm and then by the CGPS algorithm Consequently the CGPS algoshyrithm is not suitable for the 3FSS with minimum makespan criterion
REFERENCES
l H G Campbell R A Dudek and M L Smith A heuristic algorithm for the n-job tn-machine sequencing problem Management Science Vol 16 (1970) pp 630-637
2 B Chen C A Glass C N Potts and V A Strusevich A new heuristic for threeshymachine flow shop scheduling Operations Research Vol 44 (1996) pp 891-898
3 R W Conway W L Maxwell and L W Miller Theory of Scheduling AddisonshyWesley Reading MA 1967
4 R A Dudek S S Panwalkar and M L Smith The lessons of flowshop scheduling research Operations Research Vol 40 (1992) pp 7-13
5 M R Garey D S Johnson and R Sethi The complexity of flowshop and jobshop scheduling Mathematics Operations Research Vol 1 (1976) pp 117-129
6 S M Johnson Optimal two- and three-stage production schedules with setup times included Naval Research Logistic Quarterly Vol 1 (1954) pp 61-68
7 B J Lageweg J K Lenstra and A H G Rinnooy Kan A general bounding scheme for the permutation flow-shop problem Operations Research Vol 26 (1978) pp 53-67
8 E L Lawler J K Lenstra A H G Rinnooy Kan and D B Shmoys Sequencing and Scheduling Algorithms and Complexity in S C Graves A H G Rinnooy
-Kan and P H Zipkin Handbooks in Operations Research and Management Science Vol 4 Logistics of Production and Inventory North Holland Amsterdam 1993 pp 445-522
9 J K Lenstra A H G Rinnooy Kan and P Brucker Complexity of machine scheduling problems Annals Discrete Mathematics Vol 1 (1977) pp 343-362
10 M Nawaz E E Enscore Jr and I Ham A heuristic algorithm for the m-machine n-job flow-shop sequencing problem OMEGA Vol 11 (1983) pp 91-95
II C N Potts An Adaptive branching rule for the permutation flow-shop problem European Journal of Operational Research Vol 5 (1980) pp 19-25
12 A H G Rinnooy Kan Machine scheduling problems classification complexity and computations Nijhoff The Hague 1976
13 H Rock and G Schmidt Machine aggregation heuristics in shop scheduling Methods of Operations Research Vol 45 (1983) pp 303-314
14 C Smutnicki Some results of the worst-case analysis for flow shop scheduling European Journal of Operational Research Vol 109 (1998) pp 66-87
15 S M A Suliman A two-phase heuristic approach to the permutation flow-shop scheduling problem International Journal of Production Economics Vol 64 (2000) pp 143-152
16 H C Tang A new lower bonding rule for permutation flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 249-257
17 H C Tang A modification to the CGPS algorithm for three-machine flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 321-331
Received January 2001
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325 ANALYSIS OF THE CGPS ALGORITHM
For N ~ N we define a(N) =L ai The notation B(N) and c(N) are iEN
defined similarly For the 1tl obtained by applying Johnsons algorithm [6] we assume that Il and v are critical jobs where 1 S Il S v s n We also assume that
Nl = I 2 Il- I Nz = Il + 1 v - I and
N3 =v+ 1 n
Then the makespan of 1tl is rewritten as
a(Nl) + aJ1 + bfl + cJ1 + c(Ns) if Il v Cmax(n l ) (1)
ja(Nl ) + aJ1 + bJ1 + b(N2) + bv + Cv + c(Ns) if Il v
In the following we present two lemmas that are useful in the subsequent analysis
V It
LEMMA 1 Cmain) ~ L ak + bv + L Ck for v == 1 2 n (2) k=l k=y
PROOF See p 893 in Chen et a1 [2] 0
Next Chen et a1 [2] applied one value of v in (2) to establish conditions under which the schedule 1tl has a worst-case performance ratio of 53
LEMMA 2 If Il and v are critical jobs in 1tgt then
(a) If Il == v then 1tl is an optimal permutation schedule (b) If Il lt v and
beNz) + minbJ1 by S 23 beN) (3)
then we have Cmax(1tl)Cmax(1t) S 53 (4)
PROOF See p 893 11 Chen et a1 [2] 0
If Il lt v and (3) is violated Chen et a1 [2] constructed a schedule 1t2 such that the better of 1tl and 1t2 has a worst-case performance ratio of 53
LEMMA 3 If Il lt v (3) is violated a(N1) + aJ1 ~ Cy + c(N3) and (nl)Cmax (1t) ~ 53 then the permutation schedule 1tz generatedCmax
by the CGPS algorithm satisfies
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326 HCTANG
Cmax(1ti)ICmax(1tmiddot) ~ 53 (5)
PROOF See p 895 in Chen et al [2) 0
Two notable observations are obtained from the CGPS algorithm One is that the desired performance (4) sometimes holds but (3) violates The other is that the schedule 1t2 does not necessarily represent an imshyprovement on 1t1 in its makespan Detailed descriptions are presented in the following
We now concern the necessary and sufficient conditions for the desired performance (4) In Lemma 2 we can recognize that condition (3) is a sufficient for the desired performance (4) However as Chen et al [2) point out (4) sometimes holds also when (3) is violated since the lower bound used in Lemma 2 is not sufficient sharp In other words (3) is a sufficient but not necessary condition for (4) Thus we propose a modification to the CGPS algorithm by changing condition (3) While we derive new lower bounds we shall be concerned with the trade-off between the sharpness of lower bounds and their computashytional requirements As indicated in Chen et al [2] the lower bounds used in the analysis are not sufficient strong Then we shall propose some stronger lower bounds with a little extra computational requirement One way improving upon lower bound of the optimal schedule is to apply two values of v in (2)
LEMMA 4 If fl and v are critical jobs in 1t1 and fl lt v then
(a) Cmai1tmiddot) ~ plusmnak + i Ck + max b~ + ~ Ck plusmnak + bv ) (6) kl kv k=1l k=~+l
(b) We can replace (3) by
b(Ni) + bll + bv - maX[bll + ~ Ck plusmnak + b) ~ 23 beN) (7) k=~ k=IHl
(c) If condition (3) holds then (7) holds
PROOF After applying (2) with v fl and v v gives
Inequality (6) is now derived
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327 ANALYSIS OF THE CGPS ALGORITHM
To prove part (b) we subtract (6) from (1) to obtain
Cmai 1t l) - Cmai 1tmiddot) $ a(N1) + all + bit + b(Nz + by + cy+ c(Na)
Substituting (7) yields
Cmai1tl) - Cmai1tmiddot)$ 23 b(N)
The desired inequality (4) is now deduced from C (1t) b(N) Somax that we can replace (3) by (7)
Finally since
b(N2) + bit + bv - maxi bit + ~ Ck I ak + by ) w1t kdegIt+1
$ b(N2) + bit + by - max blL by)
== b(N2) + min bit by
it follows that if (3) holds then (7) holds This completes the proof of the lemma 0
Another approach for deriving a sharper lower bound of the optimal schedule is using (2) From the proof of Lemma 1 in Chem et al [2] the lower bound of an optimal schedule is
(8)
Iy n )where max L (Jk + L Pk 11 $ v $ n is the makespan of the artificial
k=l k=y
two-machine FSS with processing times Ctk == ak + bk and Pk = bk + Ck
for kEN It follows that condition (3) can be replaced by
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328 H C TANG
It is recognized that the lower bound (8) considers all values of v in (2) but the lower bound (6) only considers two of its values This implies that the lower bound (8) is stronger than (6) Therefore condition (9) is less restrictive than condition (7) This result is stated formally below
LEMMA 5 If condition (7) holds then (9) holds
Therefore among the three conditions condition (9) is the least restrictive from Lemma 4 and Lemma 5
The second notable observation is that the schedule 1t2 does not always improve the makespan of the schedule 1tl In other words the makespan of1t2 sometimes is larger than or equal to the makespan Of1tl
Taking into consideration the above pieces of information our conclusion is that eight cases arise through the various possible comshybinations For condition (3) ~s an example these cases are
(C1) 11 =v
(C2) 11 lt v and (3) holds
(C3) 11 lt v (3) is violated Cmai1tl) gt Cmai1t0 and (4) holds
(C4) 11 lt v (3) is violated Cmai1tl) gt Cmai1t2) and (4) is violated (10)
(C5) 11 lt v (3) is violated Cmai1tl) = Cmai1t2) and (4) holds
(C6) 11 lt v (3) is violated Cmai1tl) = Cmai1t0 and (4) is violated
(C7) 11 lt v (3) is violated Cmai1tl) lt Cmai1t2) and (4) holds
(C8) 11 lt v (3) is violated Cmai1tl) lt Cmai1t2) and (4) is violated
Similarly for other two proposed conditions we replace (3) by (7) and (9) respectively
3 EVALUATION MEASURES
To evaluate the performance of the CGPS algorithm this paper provides a number of evaluation measures in three respects The first respect refers to the effectiveness of the CGPS algorithm The second respect is concerned with the comparison of the three conditions satisfyshying the desired performance (4) The final respect is apout comparing with the heuristics developed by Nawaz Enscore and Ham (NEH) [10] and by Campbell Dudek and Smith (CDS) [1] which are the best two among the constructive algorithms for the FSS with minimum makespan criterion
Firstly to measure the effectiveness of the CGPS algorithm two evaluation measures are adopted In Lemma 2 for the case that
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329 ANALYSIS OF THE CGPS ALGORITHM
l == v an optimal schedule is found from the CGPS algorithm It follows that the percentage of schedules that are equal to the optimal ones is used to measure its effectiveness This proportional optimum is defined as
P [(C1)Tl 100 (11)
where (C1) denotes the number of times the schedule Xl that is equal to an optimal one in T problem instances Another evaluation measshyure is the empirical average-case performance ratio For the algorithm A and problem instance Z its quality is measured by the ratio
(12)
It is recognized that the worst-case performance ratio provides a performance guarantee which is valid for all problem instances As stated in Chen et al [2] the CGPS algorithm is by far the best one with the worst-case performance ratio as the performance measure However the Operations Research practitioners also concern the comshyputational experience of the CGPS algorithm Thus we adopt the empirical average-case performance ratio
T
~A L llA(Zi)T (13) i=l
to measure its performance empirically For notational convenience the argument Z will be omitted in Cmaix Z) and Cmax(x Z) if it is not necessary in the following
Secondly to compare conditions (3) (7) and (9) satisfying the deshysired performance (4) three evaluation measures are proposed We only consider condition (3) since the other conditions are treated simishylarly It is recognized that the desired performance (4) sometimes holds but condition (3) violates This implies that the percentage of equivalent relation between condition (3) and the desired performance (4) can be used as an evaluation measure This proportional equivashylency can be defined as
i _[ (C2) + (C4) +(C6) + (C8) ] E - (C2)+(C3)+(C4)+(C5)+(C6)+(C7)+(C8) 100
for i == 3 7 9 (14)
where (C2) + (C4) + (C6) + (C8) is thee number of times the equivalent relation between the desired performance (4) and condition (3) The other evaluation measures concern an improvement on the makespan of the schedule Xl Two evaluation measures are distinshy
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330 HCTANG
guished One is the relative error of the schedule 1tl from the schedule 1tH in terms of the 11Z)
i [ Cmax(1tH Z)Cmax(1t Z) 1R 1- 100 Cmax(1tl Z)CmaxC1t Z)
Cmax(1tH Z) ] (15) 1 - C ( l Z) 100 for pound 3 7 9[ 1tmax
The other is an improvement on the makespan of the schedule 1t1 in terms of the problem instance The definition of this proportional imshyprovement is
Ii _ [ (C3) + (C4) ] - (C3) +(C4) +(C5) +(C6) +(C7) +(C8) 100
for i 3 7 9 (16)
where (C3) + (C4) is the number of times the schedule 1tH that imshyproves the makespan of the schedule 1tl
Finally in concern with the comparison of the CGPS NEH and CDS algorithms two evaluation measures are adopted One measure is based on the empirical average-case performance ratio (13) The other measure is that the percentage of problem instances that the A algorithm is superior to the B algorithm in its makespan This proporshytional superiority can be defined as
A _[ (A) ] (17)SB - (A) +(AB) + (B) 100
where (A) and (B) are the number of times the makespan better by A and B algorithms respectively and (AB) is the number of times the makespan equal by A and B algorithms Denote E~ the percentage of problem instances that the A algorithm is equal to the B algorithm
Therefore there are six evaluation measures adopted in this paper We use (11) and (13) to measure the effectiveness of the CGPS algoshyrithm To compare conditions (3) (7) and (9) satisfying the desired pershyformance (4) three evaluation measures (14) (15) and (16) are adopted To compare the CGPS algorithm with the NEH algorithm as well as the CDS algorithm two evaluation measures (13) and (17) are used
4 SIMULATION EXPERIMENTS
To get an idea of the CGPS algorithm from the empirical perspecshytive three simulation experiments are conducted in this paper The first simulation experiment examines the effectiveness of the CGPS algoshy
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331 ANALYSIS OF THE CGPS ALGORITHM
rithm The second simulation experiment concerns the empirical comparishyson of conditions (3) (7) and (9) which are the sufficient for the desired performance (4) The third simulation experiment conducts the comparison of the CGPS algorithm with the NEH and CDS algorithms
The number of machines considered is three and the numbers of jobs considered are 3 4 5 6 7 8 9 10 15 20 30 40 50 100 200 300 400 500 600 700 800 900 and 1000 The problem size is dishyvided into two classes One is the small problem for the 3 4 5 6 7 8 9 and 10 job problems The other is the large problem for the 15 20 30 40 50 100 200 300 400 500 600 700 800 900 and 1000 job problems One hundred problems are generated for each job The processing times for each problem are randomly generated integers uniformly distributed between 1 and 100 A more portable random number generator Xn == 16807xH(mod 231
- 1) is adopted using same to generate the random values for each problem The whole simushy
lation experiments are subjected to extensive testing 13800 problems A number of evaluation measures described in previous section
are used for the three simulation experiments We adopt the evaluation measures (11) and (13) for the first simulation experiment (14) (15) and (16) for the second simulation experiment (13) and (17) for the third simulation experiment respectively The whole computation is conducted on a 266 MHz Pentinum II PC using Microsoft Visual C++ compiler under Microsoft Windows 98 operating system
To obtain an optimal schedule for the empirical average-case pershyformance ratio the branch-and-bound algorithm with two-machine lower bound rule proposed by Tang [16] is applied This proposed twoshymachine lower bound is a modification to the algorithms of Lageweg et al [7] and Potts [11] and is shown to be more efficient Whenever a problem is not solved after 100000 nodes have been generated comshyputation is stopped In this case the optimal makespan is replaced by the best-known makespan Thus the empirical average-case pershyformance ratio is underestimated
The first simulation experiment is concerned with the effectiveness of the CGPS algorithm Detailed results are presented in Table 1 where Min denotes the minimum value of proportional optimum or empirical average-case performance ratio Max and Avg the corresshyponding maximum and average values respectively The proportional optimum is 503478 on the average In other words 503478 of the initial permutation schedules generated by the CGPS algorithm are the optimal schedules especially for the large problems the empirical average-case performance ratio is concerned Table 1 lists the percentage POP of problem instances that the optimal schedules are found by the
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332 H C TANG
branch-and-bound algorithm with Tangs two-machine lower bound rule [16] The average percentage Popt is 962609 Especially in the case of small problems the branch-and-bound algorithm can always find the optimal schedules within 100000 nodes generated Therefore it is appropriate to adopt the empirical average-ca~e performance ratio to measure the effectiveness of the CGPS algorithm The empirical average-case performance ratio of the initial permutation schedule is in the range of 10011 and 10355 and is 10156 on the average While the empirical average-case performance ratio of the CGPS algorithm is in the range of 10001 and 10282 and is 10115 on the average These values are always smaller than the performance guarantee 53 of the CGPS algorithm
Table 1 Results of simulations for the effectiveness of the COPS
Job P Popt ~1t1
3 59 100 10139 4 53 100 10185 10148 5 40 100 10309 10264 6 40 100 10317 10282 7 33 100 10318 10262 8 26 100 10355 10270 9 36 100 10355 10282 10 35 100 10337 10255 15 44 84 10299 10243 20 42 82 10300 10183 30 53 90 10157 10106 40 51 94 10132 10085 50 48 95 10128 10065 100 51 93 10083 10032 200 56 97 10040 10017 300 69 99 10021 10007 400 62 98 10022 10004 500 57 96 10018 10002 600 57 99 10017 10002 700 64 96 10013 10001 800 60 99 10012 10002
(Table 1 Contd)
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333 ANALYSIS OF THE CGPS ALGORITHM
lOOO 60 97 10001
Small problems Max
Avg 59
402500 lOO lOO
10355 10289
10139 10282 10238
Large problems
Min Max
42 82 10011 10300
10001 10243 10050
Overall problems Max 69
503478 100
922609 10355 10156
10001 10282 10115
The second experiment refers to the comparison of conditions (3) (7) and (9) in terms of the proportional equivalency (14) the relative error (15) and the proportional improvement (16) Table 2 gives the reshysults Notably the number of times of the cases C3 C4 C5 C6 C7 and C8 for conditions (7) and (9) are all zeros In other words the CGPS algorithm with condition (7) or (9) only generates the initial permutashytion schedule It follows that the proportional equivalency is lOO the relative error is zero and the proportional improvement is not availshyable (NA) for each problem These results indicate that the initial pershymutation schedule never violates the worst-case performance ratio of 53 if we adopt the CGPS algorithm with condition (7) or (9)
In the case of condition (3) viz the original CGPS algorithm the proportional equivalency is 2695 on the average This value indicates that 7305 of the desired performance (4) holds also when condition (3) is violated Therefore condition (3) is too restrictive especially for the large problems In concern with the improvement on the makesshypan of 1tl the relative error is 03940 and the proportional improveshyment is 647152 on the average This implies that 647154 of the permutation schedule 1t2 has improved on the initial permutation schedule in its makespan However the quality of improvement made by 1t2 over 1tl is only 03940 in terms of the empirical average-case performance ratio Therefore condition (3) in the CGPS algorithm is more restrictive and the improvement in the average-case performance ratio is so marginal from the empirical point of view
The third experiment concerns the comparison of the CGPS algoshyrithm with the NEH algorithm as well as the CDS algorithm Comparashytive results are shown in Table 3 On basis of the empirical
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ltgtl
ltgtl
Tab
le 2
C
om
para
tiv
e r
esu
lts
for
co
nd
itio
ns
(3)
(7)
an
d (
9)
--------------shy
Job
C
ondi
tion
s E
i R
i Ii
C
1 C
2 C
3 C
4 C
5 C
6 C
7 C
8 -----shy
(3)
59
39
0 0
2 0
0 0
951
220
0
0000
000
00
3 (7
) 59
41
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 59
41
0
0 0
0 0
0 10
000
00
0
0000
NA
-shy
(3)
53
39
4 0
0 0
4 0
829
787
0
3633
500
000
4 (7
) 53
47
0
0 0
0 0
0 10
000
00
0
0000
NA
53
47
0
0 0
0 0
0 10
000
00
0
0000
NA
---shy
-----------shy
(3)
40
44
9 0
1 0
6 0
733
333
0
4365
562
500
5 (7
) 40
60
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 40
eo
0
0 0
0 0
0 10
000
00
0
0000
NA
(3
) 40
4
7
5 0
0 0
8 0
783
333
0
3392
384
600
6 (7
) 40
6
0
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
40
60
0
0 0
0 0
0 10
000
00
0
000
0
NA
-----------shy
(3)
33
37
10
0
0 0
20
0 55
223
9
054
27
33
330
0
7 (7
) 33
6
7
0 0
0 0
0 0
100
0000
000
00
N
A
p (9
) 33
6
7
0 0
0 0
0 0
100
0000
000
00
N
A
0 ------~
(Tab
le 2
Con
td)
gtshy Z
C
)
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Con
diti
ons
~ z
Job
E
i R
i Ii
gt
C
l C
2 C
3 C
4 C
5 C
6 C
7 C
8 S CD
26
40
16
0 1
0 17
0
540
541
0
8209
470
600
CD
0 gt1j
8 (7
) 26
74
0
0 0
0 0
0 10
000
00
0
0000
NA
gt-3 r
(9)
26
74
0 0
0 0
0 0
100
0000
000
00
N
A
ttl
()
36
29
12
0 1
0 22
0
453
125
0
7050
342
900
0 0
9
(7)
36
64
0 0
0 0
0 0
100
0000
000
00
N
A
CD
t
(9)
36
64
0 0
0 0
0 0
100
0000
000
00
N
A
0 ~
0 0(3
) 35
29
19
0
0 0
17
0 44
615
4
079
33
52
780
0
gt-3 r10
(7
) 35
65
0
0 0
0 0
0 10
000
00
0
0000
NA
s
(9
) 35
65
0
0 0
0 0
0 10
000
00
0
0000
NA
(3
) 44
12
20
0
0 0
24
0 21
428
6
054
37
45
450
0
15
(7)
44
56
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
44
56
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
42
9 27
0
0 0
22
0 15
517
2
113
59
55
100
0
20
(7)
42
58
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
42
58
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
53
6 19
0
0 0
22
0 12
766
0
050
21
46
340
0
30
(7)
53
47
0 0
0 0
0 0
100
0000
000
00
N
A
53
47
0 ()
0 0
0 0
100
0000
000
00
N
A
CJ
) C
J)
Of
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w
Job
--~~
~~-
~--
Ri
Ii
w
CIl
Cl
C2
C
3 C
4
C5
C6
C7
C
8 -----------shy
(3)
51
3 21
0
1 0
24
0
61
22
4
0
46
39
456
500
40
(7
) 51
4
9
0 0
0 0
0 0
10
00
00
0
0
00
00
NA
(9
) 51
4
9
0 -----_
_
-shy
0 0
0 --shy-----~~~~
0 0
-----------shy
10
00
00
0
0
00
00
---------shy
NA
(3
) 4
8
4 33
0
0 0
15
0
769
23
0
62
20
68
75
00
50
(7
) 4
8
52
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
48
5
2
0 0
0 0
0 0
100
0000
00
00
0
N
A
51
4 3
7
0 0
0 8
0 8
16
33
05
05
8
8
22
20
0
10
0
(7)
51
49
0
0 0
0 0
0 10
000
00
0
00
00
NA
(9
) 51
4
9
0 0
0 0
0 0
100
0000
00
00
0
N
A
----------shy
(3)
56
0
35
0 0
0 9
0 0
00
00
02
29
1
79
550
0
20
0
(7)
56
4
4
0 0
0 0
0 0
100
0000
00
00
0
N
A
(9)
56
4
4
0 0
0 0
0 0
100
0000
00
00
0
N
A
69
2
24
0
0 0
5 0
64
51
6
0
13
97
82
76
00
30
0
(7)
69
31
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
(9)
69
31
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
(3)
62
1
35
0
0 0
2 0
26
31
6
0
17
96
94
59
00
~
40
0
(7)
62
3
8
0 0
0 0
0 0
10
00
00
0
0
00
00
NA
0
(9)
62
3
8
0 0
0 0
0 0
100
0000
00
00
0
N
A
fja
tJte
2-C
onat
)
gt-3
)shy z Cl
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0gtshy
Con
diti
ons
Job
E
i R
i Ii
C
l C
2 C
3 C
4 C
5 C
6 C
7 C
8 ~ rt
J gt-
lt57
0
38
0 0
0 5
0 0
0000
015
97
88
370
0
rtJ
j
500
(7)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
0 3
(9)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
tr1
tlj
0(3
) 57
1
39
0 0
0 3
0 2
3256
014
97
92
860
0
0 rtJ
60
0
(7)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
~ 57
43
0
0 0
0 0
0 10
000
00
0
0000
NA
0 0
1 34
0
gt-lt
(3)
64
0 0
1 0
277
78
0
1198
971
400
tO
3
700
(7)
64
36
0 0
0 0
0 0
100
0000
000
00
N
A
tr1 ~
(9)
64
36
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
60
2 38
0
0 0
0 0
500
00
0
0999
100
0000
800
(7)
60
40
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
60
40
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
62
0 38
0
0 0
0 0
000
00
0
0999
100
0000
900
(7)
62
38
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
62
38
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
60
0 39
0
0 0
1 0
000
0
010
99
97
500
0
1000
(7
) 60
40
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 60
40
0
0 0
0 0
0 10
000
00
0
0000
NA
w
w
(T
able
2 C
ontd
)
-l
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------
CIJ
Con
diti
ons
CIJ
Job
E
i R
i Ii
O
J
C1
C2
C3
C4
C
5 C
6 C
7
C8
Min
44
615
4
000
00
0
0000
Sm
all
(3
) M
ax
951
220
0
8209
562
500
pro
ble
ms
Avg
66
121
6
050
01
39
021
3
_
Min
0
0000
009
99
45
450
0
Lar
ge
(3
) M
ax
214
286
1
1359
100
0000
p
rob
lem
s A
vg
605
84
0
3374
784
187
--
-_
__
---
Min
0
0000
000
00
0
0000
Ov
eral
l
(3)
Max
95
122
0
113
59
10
000
00
pro
ble
ms
Avg
26
950
0
039
40
64
715
2
tr1
()
gt-3 ~ Q
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gtshyT
able
3
z C
om
par
ativ
e re
sult
s fo
r th
e C
GP
S
CD
S
an
d N
EH
alg
ori
thm
s ~
Job 3
4
5
6
7
8
9
10
15
20
30
40
50
100
200
101
39
101
48
102
64
102
82
102
62
102
7
102
82
102
55
102
43
101
83
101
06
100
85
100
65
100
32
100
17
~CDS
100
96
101
28
102
14
102
32
102
46
102
15
102
04
102
04
101
78
101
53
100
99
100
63
100
55
100
35
100
17
~~--------------
~NElI
100
1
100
39
100
53
100
54
100
48
100
72
100
50
100
47
100
25
100
33
100
08
100
05
100
06
100
02
100
01
(Tab
le 3
Con
td)
lfl
gt-
lfl
0 tJ r t=J
0 0 i
lfl gtshy r 0 0 ~ r $
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-----
---
co
jgt
~CGPS
~CDS
~NE
H 0
300
100
07
100
11
100
00
400
100
04
100
06
100
00
500
100
02
100
06
100
00
600
100
02
100
03
100
00
700
100
01
100
02
100
00
800
100
02
100
02
100
00
900
100
01
100
04
100
00
1000
1
0001
1
0003
1
0000
Min
1
0139
1
0096
1
0010
Sm
all
pro
ble
ms
Max
1
0282
1
0246
1
0072
Avg
1
0238
1
0192
1
0047
Min
1
0001
1
0002
1
0000
Lar
ge
pro
ble
ms
Max
1
0243
1
0178
1
0033
Avg
1
0050
1
0042
1
0005
Min
1
0001
1
0002
1
0000
r
Ov
eral
l p
rob
lem
s M
ax
102
82
102
46
100
72
0 -3A
vg
101
15
100
95
100
20
p Z
(Tab
le 3
Con
td)
0
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lgtT
able
3 (
Con
td)
Z
--------
_shy
SCG
PS
EQ
CG
PS
SCD
S SC
GP
S E
QC
GP
S SN
EH
SN
EH
EQ
IjE
H
SCD
S --
~ Jo
b C
DS
CDS
CG
PS
NEH
N
EH
CG
PS
CD
S CD
S N
EH
en
en
3 0
91
9
0
83
17
12
88
0
0 gtzj
4 2
8
7
11
2
70
28
21
76
3
gt-l
[1j
5 2
82
16
4
58
38
28
66
6
0 0 1
j6
3
77
20
1
51
48
40
56
4
en
7 5
75
20
5
43
52
45
50
5
~ 0 0
8 6
70
24
8
35
57
44
47
9
~
gt-l
9 5
6
7
28
2
40
58
46
46
8
10
7
68
25
5
40
55
42
50
8
15
7
70
23
2
45
53
46
50
4
20
8
65
27
4
46
50
37
58
5
30
8
74
18
0
57
43
35
63
2
40
7
72
21
3
53
44
29
62
9
50
13
62
25
4
49
47
31
59
10
10
0
15
6
0
25
1
52
47
27
71
2
20
0
17
64
19
2
55
43
26
68
6
30
0
12
74
14
7
63
30
17
74
9
w
(T
able
3 C
ontd
)
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Job
S
co
Ps
CD
S E
QC
GP
SC
DS
SCD
S C
GPS
SC
GP
S N
EH
E
QC
GP
SN
EH
S
NE
H
CG
PS
NE
H
SCD
S E
Q
EH
CD
S SC
DS
NE
Il
N
400
14
63
23
5
58
37
14
81
5
500
13
62
25
1
57
42
20
78
2
60
0
9
61
30
4
54
42
12
82
6
700
11
66
23
8
56
36
13
79
8
80
0
10
63
27
4
57
39
14
82
4
900
16
63
21
2
60
38
17
81
2
1000
11
61
28
4
57
39
12
82
6
Sm
all
pro
ble
ms
Min
Max
Avg
0
67
9
7
91
28
375
00
77
125
0
191
250
0
8
337
50
35
17
83
58
525
000
44
1250
12
46
347
500
46
88
598
750
0
9
537
50
Lar
ge
pro
ble
ms
Min
Max
7
17
60
74
14
30
0
8
45
63
30
53
12
46
50
82
2
10
114
00
65
3333
2
326
67
3
4000
546
000
42
0000
233
333
71
333
3
533
33
Ov
eral
l p
rob
lem
s
Min
Max
Avg
0
60
9
17
91
30
873
91
69
434
8 2
182
61
0
8
339
13
35
17
83
58
538
696
42
739
1
12
46
273
043
~~~--~--
46
0
88
10
673
478
5
3478
c 0 ~ gtshy L
0
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343 ANALYSIS OF THE CGPS ALGORITHM
average-case performance ratio the NEH algorithm is the best one folshylowed by the CDS algorithm and then by the CGPS algorithm As comshypared with the CDS algorithm in terms of the proportional superiority 87391 of problem instances better by the CGPS algorithm 694348 of problem instances equal by the CGPS and CDS algorithms and 218261 of problem instances better by the CDS algorithm on the average It follows that the CDS algorithm is better than the CGPS alshygorithm especially for the large problems Similarly the NEH algorithm is better than the CGPS algorithm and the NEH algorithm is better than the CDS algorithm especially for the small problems Therefore we have the same conclusion about these heuristics in terms of the empirishycal average-case performance ratio and proportional superiority
To sum up 503478 ofthe initial permutation schedules generated by the CGPS algorithm are the optimal schedules Among the 496522 of non-optimal schedules 647154 of the permutation schedules 1t2 improve the makespan of the initial permutation schedule However the improvement made by 1t2 is only 03940 over 1tl in terms of the empirical average-case performance ratio Moreover compared with the NEH and CDS algorithms the CGPS algorithm is the worst one in terms of the empirical average-case performance ratio and proportional superiority These imply that the CGPS algorithm is not good enough for applications
5 CONCLUSION
In this paper the 3FSS with minimum makespan criterion is considered Theoretically the heuristic algorithm proposed by Chen et a1 [2] has the best worst-case performance ratio of 53 This paper evaluates the performance of the CGPS algorithm from the empirical perspective Three simulation experiments are performed to analyze its performance in terms of the six evaluation measures on a total of 13800 problems Four conclusions can be drawn from this paper Firstly in the CGPS algorithm 503478 of the initial permutation schedules are the optimal ones 321326 of the permutation schedshyules 1t2 improve the makespan of nl and 175196 of the permutation schedules n2 do not improve their makespan Secondly the empirical average-case performance ratio of initial permutation schedule genershyated by the CGPS algorithm is always smaller than the performance guarantee 53 developed by Chen et al [2] Its empirical average-case performance ratio is in the range of 10011 and 10355 and is 10156 on the average Thirdly the permutation schedule 1t2 improves 1tl in terms of the empirical average-case performance ratio by the non-significant percentage 03940 Fourthly among the three heuristic algorithms the
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344 H C TANG
NEH algorithm is the best one in terms of both empirical average-case performance ratio and proportional superiority followed by the CDS alshygorithm and then by the CGPS algorithm Consequently the CGPS algoshyrithm is not suitable for the 3FSS with minimum makespan criterion
REFERENCES
l H G Campbell R A Dudek and M L Smith A heuristic algorithm for the n-job tn-machine sequencing problem Management Science Vol 16 (1970) pp 630-637
2 B Chen C A Glass C N Potts and V A Strusevich A new heuristic for threeshymachine flow shop scheduling Operations Research Vol 44 (1996) pp 891-898
3 R W Conway W L Maxwell and L W Miller Theory of Scheduling AddisonshyWesley Reading MA 1967
4 R A Dudek S S Panwalkar and M L Smith The lessons of flowshop scheduling research Operations Research Vol 40 (1992) pp 7-13
5 M R Garey D S Johnson and R Sethi The complexity of flowshop and jobshop scheduling Mathematics Operations Research Vol 1 (1976) pp 117-129
6 S M Johnson Optimal two- and three-stage production schedules with setup times included Naval Research Logistic Quarterly Vol 1 (1954) pp 61-68
7 B J Lageweg J K Lenstra and A H G Rinnooy Kan A general bounding scheme for the permutation flow-shop problem Operations Research Vol 26 (1978) pp 53-67
8 E L Lawler J K Lenstra A H G Rinnooy Kan and D B Shmoys Sequencing and Scheduling Algorithms and Complexity in S C Graves A H G Rinnooy
-Kan and P H Zipkin Handbooks in Operations Research and Management Science Vol 4 Logistics of Production and Inventory North Holland Amsterdam 1993 pp 445-522
9 J K Lenstra A H G Rinnooy Kan and P Brucker Complexity of machine scheduling problems Annals Discrete Mathematics Vol 1 (1977) pp 343-362
10 M Nawaz E E Enscore Jr and I Ham A heuristic algorithm for the m-machine n-job flow-shop sequencing problem OMEGA Vol 11 (1983) pp 91-95
II C N Potts An Adaptive branching rule for the permutation flow-shop problem European Journal of Operational Research Vol 5 (1980) pp 19-25
12 A H G Rinnooy Kan Machine scheduling problems classification complexity and computations Nijhoff The Hague 1976
13 H Rock and G Schmidt Machine aggregation heuristics in shop scheduling Methods of Operations Research Vol 45 (1983) pp 303-314
14 C Smutnicki Some results of the worst-case analysis for flow shop scheduling European Journal of Operational Research Vol 109 (1998) pp 66-87
15 S M A Suliman A two-phase heuristic approach to the permutation flow-shop scheduling problem International Journal of Production Economics Vol 64 (2000) pp 143-152
16 H C Tang A new lower bonding rule for permutation flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 249-257
17 H C Tang A modification to the CGPS algorithm for three-machine flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 321-331
Received January 2001
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326 HCTANG
Cmax(1ti)ICmax(1tmiddot) ~ 53 (5)
PROOF See p 895 in Chen et al [2) 0
Two notable observations are obtained from the CGPS algorithm One is that the desired performance (4) sometimes holds but (3) violates The other is that the schedule 1t2 does not necessarily represent an imshyprovement on 1t1 in its makespan Detailed descriptions are presented in the following
We now concern the necessary and sufficient conditions for the desired performance (4) In Lemma 2 we can recognize that condition (3) is a sufficient for the desired performance (4) However as Chen et al [2) point out (4) sometimes holds also when (3) is violated since the lower bound used in Lemma 2 is not sufficient sharp In other words (3) is a sufficient but not necessary condition for (4) Thus we propose a modification to the CGPS algorithm by changing condition (3) While we derive new lower bounds we shall be concerned with the trade-off between the sharpness of lower bounds and their computashytional requirements As indicated in Chen et al [2] the lower bounds used in the analysis are not sufficient strong Then we shall propose some stronger lower bounds with a little extra computational requirement One way improving upon lower bound of the optimal schedule is to apply two values of v in (2)
LEMMA 4 If fl and v are critical jobs in 1t1 and fl lt v then
(a) Cmai1tmiddot) ~ plusmnak + i Ck + max b~ + ~ Ck plusmnak + bv ) (6) kl kv k=1l k=~+l
(b) We can replace (3) by
b(Ni) + bll + bv - maX[bll + ~ Ck plusmnak + b) ~ 23 beN) (7) k=~ k=IHl
(c) If condition (3) holds then (7) holds
PROOF After applying (2) with v fl and v v gives
Inequality (6) is now derived
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327 ANALYSIS OF THE CGPS ALGORITHM
To prove part (b) we subtract (6) from (1) to obtain
Cmai 1t l) - Cmai 1tmiddot) $ a(N1) + all + bit + b(Nz + by + cy+ c(Na)
Substituting (7) yields
Cmai1tl) - Cmai1tmiddot)$ 23 b(N)
The desired inequality (4) is now deduced from C (1t) b(N) Somax that we can replace (3) by (7)
Finally since
b(N2) + bit + bv - maxi bit + ~ Ck I ak + by ) w1t kdegIt+1
$ b(N2) + bit + by - max blL by)
== b(N2) + min bit by
it follows that if (3) holds then (7) holds This completes the proof of the lemma 0
Another approach for deriving a sharper lower bound of the optimal schedule is using (2) From the proof of Lemma 1 in Chem et al [2] the lower bound of an optimal schedule is
(8)
Iy n )where max L (Jk + L Pk 11 $ v $ n is the makespan of the artificial
k=l k=y
two-machine FSS with processing times Ctk == ak + bk and Pk = bk + Ck
for kEN It follows that condition (3) can be replaced by
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328 H C TANG
It is recognized that the lower bound (8) considers all values of v in (2) but the lower bound (6) only considers two of its values This implies that the lower bound (8) is stronger than (6) Therefore condition (9) is less restrictive than condition (7) This result is stated formally below
LEMMA 5 If condition (7) holds then (9) holds
Therefore among the three conditions condition (9) is the least restrictive from Lemma 4 and Lemma 5
The second notable observation is that the schedule 1t2 does not always improve the makespan of the schedule 1tl In other words the makespan of1t2 sometimes is larger than or equal to the makespan Of1tl
Taking into consideration the above pieces of information our conclusion is that eight cases arise through the various possible comshybinations For condition (3) ~s an example these cases are
(C1) 11 =v
(C2) 11 lt v and (3) holds
(C3) 11 lt v (3) is violated Cmai1tl) gt Cmai1t0 and (4) holds
(C4) 11 lt v (3) is violated Cmai1tl) gt Cmai1t2) and (4) is violated (10)
(C5) 11 lt v (3) is violated Cmai1tl) = Cmai1t2) and (4) holds
(C6) 11 lt v (3) is violated Cmai1tl) = Cmai1t0 and (4) is violated
(C7) 11 lt v (3) is violated Cmai1tl) lt Cmai1t2) and (4) holds
(C8) 11 lt v (3) is violated Cmai1tl) lt Cmai1t2) and (4) is violated
Similarly for other two proposed conditions we replace (3) by (7) and (9) respectively
3 EVALUATION MEASURES
To evaluate the performance of the CGPS algorithm this paper provides a number of evaluation measures in three respects The first respect refers to the effectiveness of the CGPS algorithm The second respect is concerned with the comparison of the three conditions satisfyshying the desired performance (4) The final respect is apout comparing with the heuristics developed by Nawaz Enscore and Ham (NEH) [10] and by Campbell Dudek and Smith (CDS) [1] which are the best two among the constructive algorithms for the FSS with minimum makespan criterion
Firstly to measure the effectiveness of the CGPS algorithm two evaluation measures are adopted In Lemma 2 for the case that
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329 ANALYSIS OF THE CGPS ALGORITHM
l == v an optimal schedule is found from the CGPS algorithm It follows that the percentage of schedules that are equal to the optimal ones is used to measure its effectiveness This proportional optimum is defined as
P [(C1)Tl 100 (11)
where (C1) denotes the number of times the schedule Xl that is equal to an optimal one in T problem instances Another evaluation measshyure is the empirical average-case performance ratio For the algorithm A and problem instance Z its quality is measured by the ratio
(12)
It is recognized that the worst-case performance ratio provides a performance guarantee which is valid for all problem instances As stated in Chen et al [2] the CGPS algorithm is by far the best one with the worst-case performance ratio as the performance measure However the Operations Research practitioners also concern the comshyputational experience of the CGPS algorithm Thus we adopt the empirical average-case performance ratio
T
~A L llA(Zi)T (13) i=l
to measure its performance empirically For notational convenience the argument Z will be omitted in Cmaix Z) and Cmax(x Z) if it is not necessary in the following
Secondly to compare conditions (3) (7) and (9) satisfying the deshysired performance (4) three evaluation measures are proposed We only consider condition (3) since the other conditions are treated simishylarly It is recognized that the desired performance (4) sometimes holds but condition (3) violates This implies that the percentage of equivalent relation between condition (3) and the desired performance (4) can be used as an evaluation measure This proportional equivashylency can be defined as
i _[ (C2) + (C4) +(C6) + (C8) ] E - (C2)+(C3)+(C4)+(C5)+(C6)+(C7)+(C8) 100
for i == 3 7 9 (14)
where (C2) + (C4) + (C6) + (C8) is thee number of times the equivalent relation between the desired performance (4) and condition (3) The other evaluation measures concern an improvement on the makespan of the schedule Xl Two evaluation measures are distinshy
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330 HCTANG
guished One is the relative error of the schedule 1tl from the schedule 1tH in terms of the 11Z)
i [ Cmax(1tH Z)Cmax(1t Z) 1R 1- 100 Cmax(1tl Z)CmaxC1t Z)
Cmax(1tH Z) ] (15) 1 - C ( l Z) 100 for pound 3 7 9[ 1tmax
The other is an improvement on the makespan of the schedule 1t1 in terms of the problem instance The definition of this proportional imshyprovement is
Ii _ [ (C3) + (C4) ] - (C3) +(C4) +(C5) +(C6) +(C7) +(C8) 100
for i 3 7 9 (16)
where (C3) + (C4) is the number of times the schedule 1tH that imshyproves the makespan of the schedule 1tl
Finally in concern with the comparison of the CGPS NEH and CDS algorithms two evaluation measures are adopted One measure is based on the empirical average-case performance ratio (13) The other measure is that the percentage of problem instances that the A algorithm is superior to the B algorithm in its makespan This proporshytional superiority can be defined as
A _[ (A) ] (17)SB - (A) +(AB) + (B) 100
where (A) and (B) are the number of times the makespan better by A and B algorithms respectively and (AB) is the number of times the makespan equal by A and B algorithms Denote E~ the percentage of problem instances that the A algorithm is equal to the B algorithm
Therefore there are six evaluation measures adopted in this paper We use (11) and (13) to measure the effectiveness of the CGPS algoshyrithm To compare conditions (3) (7) and (9) satisfying the desired pershyformance (4) three evaluation measures (14) (15) and (16) are adopted To compare the CGPS algorithm with the NEH algorithm as well as the CDS algorithm two evaluation measures (13) and (17) are used
4 SIMULATION EXPERIMENTS
To get an idea of the CGPS algorithm from the empirical perspecshytive three simulation experiments are conducted in this paper The first simulation experiment examines the effectiveness of the CGPS algoshy
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331 ANALYSIS OF THE CGPS ALGORITHM
rithm The second simulation experiment concerns the empirical comparishyson of conditions (3) (7) and (9) which are the sufficient for the desired performance (4) The third simulation experiment conducts the comparison of the CGPS algorithm with the NEH and CDS algorithms
The number of machines considered is three and the numbers of jobs considered are 3 4 5 6 7 8 9 10 15 20 30 40 50 100 200 300 400 500 600 700 800 900 and 1000 The problem size is dishyvided into two classes One is the small problem for the 3 4 5 6 7 8 9 and 10 job problems The other is the large problem for the 15 20 30 40 50 100 200 300 400 500 600 700 800 900 and 1000 job problems One hundred problems are generated for each job The processing times for each problem are randomly generated integers uniformly distributed between 1 and 100 A more portable random number generator Xn == 16807xH(mod 231
- 1) is adopted using same to generate the random values for each problem The whole simushy
lation experiments are subjected to extensive testing 13800 problems A number of evaluation measures described in previous section
are used for the three simulation experiments We adopt the evaluation measures (11) and (13) for the first simulation experiment (14) (15) and (16) for the second simulation experiment (13) and (17) for the third simulation experiment respectively The whole computation is conducted on a 266 MHz Pentinum II PC using Microsoft Visual C++ compiler under Microsoft Windows 98 operating system
To obtain an optimal schedule for the empirical average-case pershyformance ratio the branch-and-bound algorithm with two-machine lower bound rule proposed by Tang [16] is applied This proposed twoshymachine lower bound is a modification to the algorithms of Lageweg et al [7] and Potts [11] and is shown to be more efficient Whenever a problem is not solved after 100000 nodes have been generated comshyputation is stopped In this case the optimal makespan is replaced by the best-known makespan Thus the empirical average-case pershyformance ratio is underestimated
The first simulation experiment is concerned with the effectiveness of the CGPS algorithm Detailed results are presented in Table 1 where Min denotes the minimum value of proportional optimum or empirical average-case performance ratio Max and Avg the corresshyponding maximum and average values respectively The proportional optimum is 503478 on the average In other words 503478 of the initial permutation schedules generated by the CGPS algorithm are the optimal schedules especially for the large problems the empirical average-case performance ratio is concerned Table 1 lists the percentage POP of problem instances that the optimal schedules are found by the
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332 H C TANG
branch-and-bound algorithm with Tangs two-machine lower bound rule [16] The average percentage Popt is 962609 Especially in the case of small problems the branch-and-bound algorithm can always find the optimal schedules within 100000 nodes generated Therefore it is appropriate to adopt the empirical average-ca~e performance ratio to measure the effectiveness of the CGPS algorithm The empirical average-case performance ratio of the initial permutation schedule is in the range of 10011 and 10355 and is 10156 on the average While the empirical average-case performance ratio of the CGPS algorithm is in the range of 10001 and 10282 and is 10115 on the average These values are always smaller than the performance guarantee 53 of the CGPS algorithm
Table 1 Results of simulations for the effectiveness of the COPS
Job P Popt ~1t1
3 59 100 10139 4 53 100 10185 10148 5 40 100 10309 10264 6 40 100 10317 10282 7 33 100 10318 10262 8 26 100 10355 10270 9 36 100 10355 10282 10 35 100 10337 10255 15 44 84 10299 10243 20 42 82 10300 10183 30 53 90 10157 10106 40 51 94 10132 10085 50 48 95 10128 10065 100 51 93 10083 10032 200 56 97 10040 10017 300 69 99 10021 10007 400 62 98 10022 10004 500 57 96 10018 10002 600 57 99 10017 10002 700 64 96 10013 10001 800 60 99 10012 10002
(Table 1 Contd)
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333 ANALYSIS OF THE CGPS ALGORITHM
lOOO 60 97 10001
Small problems Max
Avg 59
402500 lOO lOO
10355 10289
10139 10282 10238
Large problems
Min Max
42 82 10011 10300
10001 10243 10050
Overall problems Max 69
503478 100
922609 10355 10156
10001 10282 10115
The second experiment refers to the comparison of conditions (3) (7) and (9) in terms of the proportional equivalency (14) the relative error (15) and the proportional improvement (16) Table 2 gives the reshysults Notably the number of times of the cases C3 C4 C5 C6 C7 and C8 for conditions (7) and (9) are all zeros In other words the CGPS algorithm with condition (7) or (9) only generates the initial permutashytion schedule It follows that the proportional equivalency is lOO the relative error is zero and the proportional improvement is not availshyable (NA) for each problem These results indicate that the initial pershymutation schedule never violates the worst-case performance ratio of 53 if we adopt the CGPS algorithm with condition (7) or (9)
In the case of condition (3) viz the original CGPS algorithm the proportional equivalency is 2695 on the average This value indicates that 7305 of the desired performance (4) holds also when condition (3) is violated Therefore condition (3) is too restrictive especially for the large problems In concern with the improvement on the makesshypan of 1tl the relative error is 03940 and the proportional improveshyment is 647152 on the average This implies that 647154 of the permutation schedule 1t2 has improved on the initial permutation schedule in its makespan However the quality of improvement made by 1t2 over 1tl is only 03940 in terms of the empirical average-case performance ratio Therefore condition (3) in the CGPS algorithm is more restrictive and the improvement in the average-case performance ratio is so marginal from the empirical point of view
The third experiment concerns the comparison of the CGPS algoshyrithm with the NEH algorithm as well as the CDS algorithm Comparashytive results are shown in Table 3 On basis of the empirical
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ltgtl
ltgtl
Tab
le 2
C
om
para
tiv
e r
esu
lts
for
co
nd
itio
ns
(3)
(7)
an
d (
9)
--------------shy
Job
C
ondi
tion
s E
i R
i Ii
C
1 C
2 C
3 C
4 C
5 C
6 C
7 C
8 -----shy
(3)
59
39
0 0
2 0
0 0
951
220
0
0000
000
00
3 (7
) 59
41
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 59
41
0
0 0
0 0
0 10
000
00
0
0000
NA
-shy
(3)
53
39
4 0
0 0
4 0
829
787
0
3633
500
000
4 (7
) 53
47
0
0 0
0 0
0 10
000
00
0
0000
NA
53
47
0
0 0
0 0
0 10
000
00
0
0000
NA
---shy
-----------shy
(3)
40
44
9 0
1 0
6 0
733
333
0
4365
562
500
5 (7
) 40
60
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 40
eo
0
0 0
0 0
0 10
000
00
0
0000
NA
(3
) 40
4
7
5 0
0 0
8 0
783
333
0
3392
384
600
6 (7
) 40
6
0
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
40
60
0
0 0
0 0
0 10
000
00
0
000
0
NA
-----------shy
(3)
33
37
10
0
0 0
20
0 55
223
9
054
27
33
330
0
7 (7
) 33
6
7
0 0
0 0
0 0
100
0000
000
00
N
A
p (9
) 33
6
7
0 0
0 0
0 0
100
0000
000
00
N
A
0 ------~
(Tab
le 2
Con
td)
gtshy Z
C
)
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Con
diti
ons
~ z
Job
E
i R
i Ii
gt
C
l C
2 C
3 C
4 C
5 C
6 C
7 C
8 S CD
26
40
16
0 1
0 17
0
540
541
0
8209
470
600
CD
0 gt1j
8 (7
) 26
74
0
0 0
0 0
0 10
000
00
0
0000
NA
gt-3 r
(9)
26
74
0 0
0 0
0 0
100
0000
000
00
N
A
ttl
()
36
29
12
0 1
0 22
0
453
125
0
7050
342
900
0 0
9
(7)
36
64
0 0
0 0
0 0
100
0000
000
00
N
A
CD
t
(9)
36
64
0 0
0 0
0 0
100
0000
000
00
N
A
0 ~
0 0(3
) 35
29
19
0
0 0
17
0 44
615
4
079
33
52
780
0
gt-3 r10
(7
) 35
65
0
0 0
0 0
0 10
000
00
0
0000
NA
s
(9
) 35
65
0
0 0
0 0
0 10
000
00
0
0000
NA
(3
) 44
12
20
0
0 0
24
0 21
428
6
054
37
45
450
0
15
(7)
44
56
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
44
56
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
42
9 27
0
0 0
22
0 15
517
2
113
59
55
100
0
20
(7)
42
58
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
42
58
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
53
6 19
0
0 0
22
0 12
766
0
050
21
46
340
0
30
(7)
53
47
0 0
0 0
0 0
100
0000
000
00
N
A
53
47
0 ()
0 0
0 0
100
0000
000
00
N
A
CJ
) C
J)
Of
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w
Job
--~~
~~-
~--
Ri
Ii
w
CIl
Cl
C2
C
3 C
4
C5
C6
C7
C
8 -----------shy
(3)
51
3 21
0
1 0
24
0
61
22
4
0
46
39
456
500
40
(7
) 51
4
9
0 0
0 0
0 0
10
00
00
0
0
00
00
NA
(9
) 51
4
9
0 -----_
_
-shy
0 0
0 --shy-----~~~~
0 0
-----------shy
10
00
00
0
0
00
00
---------shy
NA
(3
) 4
8
4 33
0
0 0
15
0
769
23
0
62
20
68
75
00
50
(7
) 4
8
52
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
48
5
2
0 0
0 0
0 0
100
0000
00
00
0
N
A
51
4 3
7
0 0
0 8
0 8
16
33
05
05
8
8
22
20
0
10
0
(7)
51
49
0
0 0
0 0
0 10
000
00
0
00
00
NA
(9
) 51
4
9
0 0
0 0
0 0
100
0000
00
00
0
N
A
----------shy
(3)
56
0
35
0 0
0 9
0 0
00
00
02
29
1
79
550
0
20
0
(7)
56
4
4
0 0
0 0
0 0
100
0000
00
00
0
N
A
(9)
56
4
4
0 0
0 0
0 0
100
0000
00
00
0
N
A
69
2
24
0
0 0
5 0
64
51
6
0
13
97
82
76
00
30
0
(7)
69
31
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
(9)
69
31
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
(3)
62
1
35
0
0 0
2 0
26
31
6
0
17
96
94
59
00
~
40
0
(7)
62
3
8
0 0
0 0
0 0
10
00
00
0
0
00
00
NA
0
(9)
62
3
8
0 0
0 0
0 0
100
0000
00
00
0
N
A
fja
tJte
2-C
onat
)
gt-3
)shy z Cl
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0gtshy
Con
diti
ons
Job
E
i R
i Ii
C
l C
2 C
3 C
4 C
5 C
6 C
7 C
8 ~ rt
J gt-
lt57
0
38
0 0
0 5
0 0
0000
015
97
88
370
0
rtJ
j
500
(7)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
0 3
(9)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
tr1
tlj
0(3
) 57
1
39
0 0
0 3
0 2
3256
014
97
92
860
0
0 rtJ
60
0
(7)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
~ 57
43
0
0 0
0 0
0 10
000
00
0
0000
NA
0 0
1 34
0
gt-lt
(3)
64
0 0
1 0
277
78
0
1198
971
400
tO
3
700
(7)
64
36
0 0
0 0
0 0
100
0000
000
00
N
A
tr1 ~
(9)
64
36
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
60
2 38
0
0 0
0 0
500
00
0
0999
100
0000
800
(7)
60
40
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
60
40
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
62
0 38
0
0 0
0 0
000
00
0
0999
100
0000
900
(7)
62
38
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
62
38
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
60
0 39
0
0 0
1 0
000
0
010
99
97
500
0
1000
(7
) 60
40
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 60
40
0
0 0
0 0
0 10
000
00
0
0000
NA
w
w
(T
able
2 C
ontd
)
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------
CIJ
Con
diti
ons
CIJ
Job
E
i R
i Ii
O
J
C1
C2
C3
C4
C
5 C
6 C
7
C8
Min
44
615
4
000
00
0
0000
Sm
all
(3
) M
ax
951
220
0
8209
562
500
pro
ble
ms
Avg
66
121
6
050
01
39
021
3
_
Min
0
0000
009
99
45
450
0
Lar
ge
(3
) M
ax
214
286
1
1359
100
0000
p
rob
lem
s A
vg
605
84
0
3374
784
187
--
-_
__
---
Min
0
0000
000
00
0
0000
Ov
eral
l
(3)
Max
95
122
0
113
59
10
000
00
pro
ble
ms
Avg
26
950
0
039
40
64
715
2
tr1
()
gt-3 ~ Q
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gtshyT
able
3
z C
om
par
ativ
e re
sult
s fo
r th
e C
GP
S
CD
S
an
d N
EH
alg
ori
thm
s ~
Job 3
4
5
6
7
8
9
10
15
20
30
40
50
100
200
101
39
101
48
102
64
102
82
102
62
102
7
102
82
102
55
102
43
101
83
101
06
100
85
100
65
100
32
100
17
~CDS
100
96
101
28
102
14
102
32
102
46
102
15
102
04
102
04
101
78
101
53
100
99
100
63
100
55
100
35
100
17
~~--------------
~NElI
100
1
100
39
100
53
100
54
100
48
100
72
100
50
100
47
100
25
100
33
100
08
100
05
100
06
100
02
100
01
(Tab
le 3
Con
td)
lfl
gt-
lfl
0 tJ r t=J
0 0 i
lfl gtshy r 0 0 ~ r $
w
w
to
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-----
---
co
jgt
~CGPS
~CDS
~NE
H 0
300
100
07
100
11
100
00
400
100
04
100
06
100
00
500
100
02
100
06
100
00
600
100
02
100
03
100
00
700
100
01
100
02
100
00
800
100
02
100
02
100
00
900
100
01
100
04
100
00
1000
1
0001
1
0003
1
0000
Min
1
0139
1
0096
1
0010
Sm
all
pro
ble
ms
Max
1
0282
1
0246
1
0072
Avg
1
0238
1
0192
1
0047
Min
1
0001
1
0002
1
0000
Lar
ge
pro
ble
ms
Max
1
0243
1
0178
1
0033
Avg
1
0050
1
0042
1
0005
Min
1
0001
1
0002
1
0000
r
Ov
eral
l p
rob
lem
s M
ax
102
82
102
46
100
72
0 -3A
vg
101
15
100
95
100
20
p Z
(Tab
le 3
Con
td)
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lgtT
able
3 (
Con
td)
Z
--------
_shy
SCG
PS
EQ
CG
PS
SCD
S SC
GP
S E
QC
GP
S SN
EH
SN
EH
EQ
IjE
H
SCD
S --
~ Jo
b C
DS
CDS
CG
PS
NEH
N
EH
CG
PS
CD
S CD
S N
EH
en
en
3 0
91
9
0
83
17
12
88
0
0 gtzj
4 2
8
7
11
2
70
28
21
76
3
gt-l
[1j
5 2
82
16
4
58
38
28
66
6
0 0 1
j6
3
77
20
1
51
48
40
56
4
en
7 5
75
20
5
43
52
45
50
5
~ 0 0
8 6
70
24
8
35
57
44
47
9
~
gt-l
9 5
6
7
28
2
40
58
46
46
8
10
7
68
25
5
40
55
42
50
8
15
7
70
23
2
45
53
46
50
4
20
8
65
27
4
46
50
37
58
5
30
8
74
18
0
57
43
35
63
2
40
7
72
21
3
53
44
29
62
9
50
13
62
25
4
49
47
31
59
10
10
0
15
6
0
25
1
52
47
27
71
2
20
0
17
64
19
2
55
43
26
68
6
30
0
12
74
14
7
63
30
17
74
9
w
(T
able
3 C
ontd
)
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Job
S
co
Ps
CD
S E
QC
GP
SC
DS
SCD
S C
GPS
SC
GP
S N
EH
E
QC
GP
SN
EH
S
NE
H
CG
PS
NE
H
SCD
S E
Q
EH
CD
S SC
DS
NE
Il
N
400
14
63
23
5
58
37
14
81
5
500
13
62
25
1
57
42
20
78
2
60
0
9
61
30
4
54
42
12
82
6
700
11
66
23
8
56
36
13
79
8
80
0
10
63
27
4
57
39
14
82
4
900
16
63
21
2
60
38
17
81
2
1000
11
61
28
4
57
39
12
82
6
Sm
all
pro
ble
ms
Min
Max
Avg
0
67
9
7
91
28
375
00
77
125
0
191
250
0
8
337
50
35
17
83
58
525
000
44
1250
12
46
347
500
46
88
598
750
0
9
537
50
Lar
ge
pro
ble
ms
Min
Max
7
17
60
74
14
30
0
8
45
63
30
53
12
46
50
82
2
10
114
00
65
3333
2
326
67
3
4000
546
000
42
0000
233
333
71
333
3
533
33
Ov
eral
l p
rob
lem
s
Min
Max
Avg
0
60
9
17
91
30
873
91
69
434
8 2
182
61
0
8
339
13
35
17
83
58
538
696
42
739
1
12
46
273
043
~~~--~--
46
0
88
10
673
478
5
3478
c 0 ~ gtshy L
0
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343 ANALYSIS OF THE CGPS ALGORITHM
average-case performance ratio the NEH algorithm is the best one folshylowed by the CDS algorithm and then by the CGPS algorithm As comshypared with the CDS algorithm in terms of the proportional superiority 87391 of problem instances better by the CGPS algorithm 694348 of problem instances equal by the CGPS and CDS algorithms and 218261 of problem instances better by the CDS algorithm on the average It follows that the CDS algorithm is better than the CGPS alshygorithm especially for the large problems Similarly the NEH algorithm is better than the CGPS algorithm and the NEH algorithm is better than the CDS algorithm especially for the small problems Therefore we have the same conclusion about these heuristics in terms of the empirishycal average-case performance ratio and proportional superiority
To sum up 503478 ofthe initial permutation schedules generated by the CGPS algorithm are the optimal schedules Among the 496522 of non-optimal schedules 647154 of the permutation schedules 1t2 improve the makespan of the initial permutation schedule However the improvement made by 1t2 is only 03940 over 1tl in terms of the empirical average-case performance ratio Moreover compared with the NEH and CDS algorithms the CGPS algorithm is the worst one in terms of the empirical average-case performance ratio and proportional superiority These imply that the CGPS algorithm is not good enough for applications
5 CONCLUSION
In this paper the 3FSS with minimum makespan criterion is considered Theoretically the heuristic algorithm proposed by Chen et a1 [2] has the best worst-case performance ratio of 53 This paper evaluates the performance of the CGPS algorithm from the empirical perspective Three simulation experiments are performed to analyze its performance in terms of the six evaluation measures on a total of 13800 problems Four conclusions can be drawn from this paper Firstly in the CGPS algorithm 503478 of the initial permutation schedules are the optimal ones 321326 of the permutation schedshyules 1t2 improve the makespan of nl and 175196 of the permutation schedules n2 do not improve their makespan Secondly the empirical average-case performance ratio of initial permutation schedule genershyated by the CGPS algorithm is always smaller than the performance guarantee 53 developed by Chen et al [2] Its empirical average-case performance ratio is in the range of 10011 and 10355 and is 10156 on the average Thirdly the permutation schedule 1t2 improves 1tl in terms of the empirical average-case performance ratio by the non-significant percentage 03940 Fourthly among the three heuristic algorithms the
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344 H C TANG
NEH algorithm is the best one in terms of both empirical average-case performance ratio and proportional superiority followed by the CDS alshygorithm and then by the CGPS algorithm Consequently the CGPS algoshyrithm is not suitable for the 3FSS with minimum makespan criterion
REFERENCES
l H G Campbell R A Dudek and M L Smith A heuristic algorithm for the n-job tn-machine sequencing problem Management Science Vol 16 (1970) pp 630-637
2 B Chen C A Glass C N Potts and V A Strusevich A new heuristic for threeshymachine flow shop scheduling Operations Research Vol 44 (1996) pp 891-898
3 R W Conway W L Maxwell and L W Miller Theory of Scheduling AddisonshyWesley Reading MA 1967
4 R A Dudek S S Panwalkar and M L Smith The lessons of flowshop scheduling research Operations Research Vol 40 (1992) pp 7-13
5 M R Garey D S Johnson and R Sethi The complexity of flowshop and jobshop scheduling Mathematics Operations Research Vol 1 (1976) pp 117-129
6 S M Johnson Optimal two- and three-stage production schedules with setup times included Naval Research Logistic Quarterly Vol 1 (1954) pp 61-68
7 B J Lageweg J K Lenstra and A H G Rinnooy Kan A general bounding scheme for the permutation flow-shop problem Operations Research Vol 26 (1978) pp 53-67
8 E L Lawler J K Lenstra A H G Rinnooy Kan and D B Shmoys Sequencing and Scheduling Algorithms and Complexity in S C Graves A H G Rinnooy
-Kan and P H Zipkin Handbooks in Operations Research and Management Science Vol 4 Logistics of Production and Inventory North Holland Amsterdam 1993 pp 445-522
9 J K Lenstra A H G Rinnooy Kan and P Brucker Complexity of machine scheduling problems Annals Discrete Mathematics Vol 1 (1977) pp 343-362
10 M Nawaz E E Enscore Jr and I Ham A heuristic algorithm for the m-machine n-job flow-shop sequencing problem OMEGA Vol 11 (1983) pp 91-95
II C N Potts An Adaptive branching rule for the permutation flow-shop problem European Journal of Operational Research Vol 5 (1980) pp 19-25
12 A H G Rinnooy Kan Machine scheduling problems classification complexity and computations Nijhoff The Hague 1976
13 H Rock and G Schmidt Machine aggregation heuristics in shop scheduling Methods of Operations Research Vol 45 (1983) pp 303-314
14 C Smutnicki Some results of the worst-case analysis for flow shop scheduling European Journal of Operational Research Vol 109 (1998) pp 66-87
15 S M A Suliman A two-phase heuristic approach to the permutation flow-shop scheduling problem International Journal of Production Economics Vol 64 (2000) pp 143-152
16 H C Tang A new lower bonding rule for permutation flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 249-257
17 H C Tang A modification to the CGPS algorithm for three-machine flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 321-331
Received January 2001
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327 ANALYSIS OF THE CGPS ALGORITHM
To prove part (b) we subtract (6) from (1) to obtain
Cmai 1t l) - Cmai 1tmiddot) $ a(N1) + all + bit + b(Nz + by + cy+ c(Na)
Substituting (7) yields
Cmai1tl) - Cmai1tmiddot)$ 23 b(N)
The desired inequality (4) is now deduced from C (1t) b(N) Somax that we can replace (3) by (7)
Finally since
b(N2) + bit + bv - maxi bit + ~ Ck I ak + by ) w1t kdegIt+1
$ b(N2) + bit + by - max blL by)
== b(N2) + min bit by
it follows that if (3) holds then (7) holds This completes the proof of the lemma 0
Another approach for deriving a sharper lower bound of the optimal schedule is using (2) From the proof of Lemma 1 in Chem et al [2] the lower bound of an optimal schedule is
(8)
Iy n )where max L (Jk + L Pk 11 $ v $ n is the makespan of the artificial
k=l k=y
two-machine FSS with processing times Ctk == ak + bk and Pk = bk + Ck
for kEN It follows that condition (3) can be replaced by
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328 H C TANG
It is recognized that the lower bound (8) considers all values of v in (2) but the lower bound (6) only considers two of its values This implies that the lower bound (8) is stronger than (6) Therefore condition (9) is less restrictive than condition (7) This result is stated formally below
LEMMA 5 If condition (7) holds then (9) holds
Therefore among the three conditions condition (9) is the least restrictive from Lemma 4 and Lemma 5
The second notable observation is that the schedule 1t2 does not always improve the makespan of the schedule 1tl In other words the makespan of1t2 sometimes is larger than or equal to the makespan Of1tl
Taking into consideration the above pieces of information our conclusion is that eight cases arise through the various possible comshybinations For condition (3) ~s an example these cases are
(C1) 11 =v
(C2) 11 lt v and (3) holds
(C3) 11 lt v (3) is violated Cmai1tl) gt Cmai1t0 and (4) holds
(C4) 11 lt v (3) is violated Cmai1tl) gt Cmai1t2) and (4) is violated (10)
(C5) 11 lt v (3) is violated Cmai1tl) = Cmai1t2) and (4) holds
(C6) 11 lt v (3) is violated Cmai1tl) = Cmai1t0 and (4) is violated
(C7) 11 lt v (3) is violated Cmai1tl) lt Cmai1t2) and (4) holds
(C8) 11 lt v (3) is violated Cmai1tl) lt Cmai1t2) and (4) is violated
Similarly for other two proposed conditions we replace (3) by (7) and (9) respectively
3 EVALUATION MEASURES
To evaluate the performance of the CGPS algorithm this paper provides a number of evaluation measures in three respects The first respect refers to the effectiveness of the CGPS algorithm The second respect is concerned with the comparison of the three conditions satisfyshying the desired performance (4) The final respect is apout comparing with the heuristics developed by Nawaz Enscore and Ham (NEH) [10] and by Campbell Dudek and Smith (CDS) [1] which are the best two among the constructive algorithms for the FSS with minimum makespan criterion
Firstly to measure the effectiveness of the CGPS algorithm two evaluation measures are adopted In Lemma 2 for the case that
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329 ANALYSIS OF THE CGPS ALGORITHM
l == v an optimal schedule is found from the CGPS algorithm It follows that the percentage of schedules that are equal to the optimal ones is used to measure its effectiveness This proportional optimum is defined as
P [(C1)Tl 100 (11)
where (C1) denotes the number of times the schedule Xl that is equal to an optimal one in T problem instances Another evaluation measshyure is the empirical average-case performance ratio For the algorithm A and problem instance Z its quality is measured by the ratio
(12)
It is recognized that the worst-case performance ratio provides a performance guarantee which is valid for all problem instances As stated in Chen et al [2] the CGPS algorithm is by far the best one with the worst-case performance ratio as the performance measure However the Operations Research practitioners also concern the comshyputational experience of the CGPS algorithm Thus we adopt the empirical average-case performance ratio
T
~A L llA(Zi)T (13) i=l
to measure its performance empirically For notational convenience the argument Z will be omitted in Cmaix Z) and Cmax(x Z) if it is not necessary in the following
Secondly to compare conditions (3) (7) and (9) satisfying the deshysired performance (4) three evaluation measures are proposed We only consider condition (3) since the other conditions are treated simishylarly It is recognized that the desired performance (4) sometimes holds but condition (3) violates This implies that the percentage of equivalent relation between condition (3) and the desired performance (4) can be used as an evaluation measure This proportional equivashylency can be defined as
i _[ (C2) + (C4) +(C6) + (C8) ] E - (C2)+(C3)+(C4)+(C5)+(C6)+(C7)+(C8) 100
for i == 3 7 9 (14)
where (C2) + (C4) + (C6) + (C8) is thee number of times the equivalent relation between the desired performance (4) and condition (3) The other evaluation measures concern an improvement on the makespan of the schedule Xl Two evaluation measures are distinshy
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330 HCTANG
guished One is the relative error of the schedule 1tl from the schedule 1tH in terms of the 11Z)
i [ Cmax(1tH Z)Cmax(1t Z) 1R 1- 100 Cmax(1tl Z)CmaxC1t Z)
Cmax(1tH Z) ] (15) 1 - C ( l Z) 100 for pound 3 7 9[ 1tmax
The other is an improvement on the makespan of the schedule 1t1 in terms of the problem instance The definition of this proportional imshyprovement is
Ii _ [ (C3) + (C4) ] - (C3) +(C4) +(C5) +(C6) +(C7) +(C8) 100
for i 3 7 9 (16)
where (C3) + (C4) is the number of times the schedule 1tH that imshyproves the makespan of the schedule 1tl
Finally in concern with the comparison of the CGPS NEH and CDS algorithms two evaluation measures are adopted One measure is based on the empirical average-case performance ratio (13) The other measure is that the percentage of problem instances that the A algorithm is superior to the B algorithm in its makespan This proporshytional superiority can be defined as
A _[ (A) ] (17)SB - (A) +(AB) + (B) 100
where (A) and (B) are the number of times the makespan better by A and B algorithms respectively and (AB) is the number of times the makespan equal by A and B algorithms Denote E~ the percentage of problem instances that the A algorithm is equal to the B algorithm
Therefore there are six evaluation measures adopted in this paper We use (11) and (13) to measure the effectiveness of the CGPS algoshyrithm To compare conditions (3) (7) and (9) satisfying the desired pershyformance (4) three evaluation measures (14) (15) and (16) are adopted To compare the CGPS algorithm with the NEH algorithm as well as the CDS algorithm two evaluation measures (13) and (17) are used
4 SIMULATION EXPERIMENTS
To get an idea of the CGPS algorithm from the empirical perspecshytive three simulation experiments are conducted in this paper The first simulation experiment examines the effectiveness of the CGPS algoshy
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331 ANALYSIS OF THE CGPS ALGORITHM
rithm The second simulation experiment concerns the empirical comparishyson of conditions (3) (7) and (9) which are the sufficient for the desired performance (4) The third simulation experiment conducts the comparison of the CGPS algorithm with the NEH and CDS algorithms
The number of machines considered is three and the numbers of jobs considered are 3 4 5 6 7 8 9 10 15 20 30 40 50 100 200 300 400 500 600 700 800 900 and 1000 The problem size is dishyvided into two classes One is the small problem for the 3 4 5 6 7 8 9 and 10 job problems The other is the large problem for the 15 20 30 40 50 100 200 300 400 500 600 700 800 900 and 1000 job problems One hundred problems are generated for each job The processing times for each problem are randomly generated integers uniformly distributed between 1 and 100 A more portable random number generator Xn == 16807xH(mod 231
- 1) is adopted using same to generate the random values for each problem The whole simushy
lation experiments are subjected to extensive testing 13800 problems A number of evaluation measures described in previous section
are used for the three simulation experiments We adopt the evaluation measures (11) and (13) for the first simulation experiment (14) (15) and (16) for the second simulation experiment (13) and (17) for the third simulation experiment respectively The whole computation is conducted on a 266 MHz Pentinum II PC using Microsoft Visual C++ compiler under Microsoft Windows 98 operating system
To obtain an optimal schedule for the empirical average-case pershyformance ratio the branch-and-bound algorithm with two-machine lower bound rule proposed by Tang [16] is applied This proposed twoshymachine lower bound is a modification to the algorithms of Lageweg et al [7] and Potts [11] and is shown to be more efficient Whenever a problem is not solved after 100000 nodes have been generated comshyputation is stopped In this case the optimal makespan is replaced by the best-known makespan Thus the empirical average-case pershyformance ratio is underestimated
The first simulation experiment is concerned with the effectiveness of the CGPS algorithm Detailed results are presented in Table 1 where Min denotes the minimum value of proportional optimum or empirical average-case performance ratio Max and Avg the corresshyponding maximum and average values respectively The proportional optimum is 503478 on the average In other words 503478 of the initial permutation schedules generated by the CGPS algorithm are the optimal schedules especially for the large problems the empirical average-case performance ratio is concerned Table 1 lists the percentage POP of problem instances that the optimal schedules are found by the
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332 H C TANG
branch-and-bound algorithm with Tangs two-machine lower bound rule [16] The average percentage Popt is 962609 Especially in the case of small problems the branch-and-bound algorithm can always find the optimal schedules within 100000 nodes generated Therefore it is appropriate to adopt the empirical average-ca~e performance ratio to measure the effectiveness of the CGPS algorithm The empirical average-case performance ratio of the initial permutation schedule is in the range of 10011 and 10355 and is 10156 on the average While the empirical average-case performance ratio of the CGPS algorithm is in the range of 10001 and 10282 and is 10115 on the average These values are always smaller than the performance guarantee 53 of the CGPS algorithm
Table 1 Results of simulations for the effectiveness of the COPS
Job P Popt ~1t1
3 59 100 10139 4 53 100 10185 10148 5 40 100 10309 10264 6 40 100 10317 10282 7 33 100 10318 10262 8 26 100 10355 10270 9 36 100 10355 10282 10 35 100 10337 10255 15 44 84 10299 10243 20 42 82 10300 10183 30 53 90 10157 10106 40 51 94 10132 10085 50 48 95 10128 10065 100 51 93 10083 10032 200 56 97 10040 10017 300 69 99 10021 10007 400 62 98 10022 10004 500 57 96 10018 10002 600 57 99 10017 10002 700 64 96 10013 10001 800 60 99 10012 10002
(Table 1 Contd)
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333 ANALYSIS OF THE CGPS ALGORITHM
lOOO 60 97 10001
Small problems Max
Avg 59
402500 lOO lOO
10355 10289
10139 10282 10238
Large problems
Min Max
42 82 10011 10300
10001 10243 10050
Overall problems Max 69
503478 100
922609 10355 10156
10001 10282 10115
The second experiment refers to the comparison of conditions (3) (7) and (9) in terms of the proportional equivalency (14) the relative error (15) and the proportional improvement (16) Table 2 gives the reshysults Notably the number of times of the cases C3 C4 C5 C6 C7 and C8 for conditions (7) and (9) are all zeros In other words the CGPS algorithm with condition (7) or (9) only generates the initial permutashytion schedule It follows that the proportional equivalency is lOO the relative error is zero and the proportional improvement is not availshyable (NA) for each problem These results indicate that the initial pershymutation schedule never violates the worst-case performance ratio of 53 if we adopt the CGPS algorithm with condition (7) or (9)
In the case of condition (3) viz the original CGPS algorithm the proportional equivalency is 2695 on the average This value indicates that 7305 of the desired performance (4) holds also when condition (3) is violated Therefore condition (3) is too restrictive especially for the large problems In concern with the improvement on the makesshypan of 1tl the relative error is 03940 and the proportional improveshyment is 647152 on the average This implies that 647154 of the permutation schedule 1t2 has improved on the initial permutation schedule in its makespan However the quality of improvement made by 1t2 over 1tl is only 03940 in terms of the empirical average-case performance ratio Therefore condition (3) in the CGPS algorithm is more restrictive and the improvement in the average-case performance ratio is so marginal from the empirical point of view
The third experiment concerns the comparison of the CGPS algoshyrithm with the NEH algorithm as well as the CDS algorithm Comparashytive results are shown in Table 3 On basis of the empirical
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ltgtl
ltgtl
Tab
le 2
C
om
para
tiv
e r
esu
lts
for
co
nd
itio
ns
(3)
(7)
an
d (
9)
--------------shy
Job
C
ondi
tion
s E
i R
i Ii
C
1 C
2 C
3 C
4 C
5 C
6 C
7 C
8 -----shy
(3)
59
39
0 0
2 0
0 0
951
220
0
0000
000
00
3 (7
) 59
41
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 59
41
0
0 0
0 0
0 10
000
00
0
0000
NA
-shy
(3)
53
39
4 0
0 0
4 0
829
787
0
3633
500
000
4 (7
) 53
47
0
0 0
0 0
0 10
000
00
0
0000
NA
53
47
0
0 0
0 0
0 10
000
00
0
0000
NA
---shy
-----------shy
(3)
40
44
9 0
1 0
6 0
733
333
0
4365
562
500
5 (7
) 40
60
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 40
eo
0
0 0
0 0
0 10
000
00
0
0000
NA
(3
) 40
4
7
5 0
0 0
8 0
783
333
0
3392
384
600
6 (7
) 40
6
0
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
40
60
0
0 0
0 0
0 10
000
00
0
000
0
NA
-----------shy
(3)
33
37
10
0
0 0
20
0 55
223
9
054
27
33
330
0
7 (7
) 33
6
7
0 0
0 0
0 0
100
0000
000
00
N
A
p (9
) 33
6
7
0 0
0 0
0 0
100
0000
000
00
N
A
0 ------~
(Tab
le 2
Con
td)
gtshy Z
C
)
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Con
diti
ons
~ z
Job
E
i R
i Ii
gt
C
l C
2 C
3 C
4 C
5 C
6 C
7 C
8 S CD
26
40
16
0 1
0 17
0
540
541
0
8209
470
600
CD
0 gt1j
8 (7
) 26
74
0
0 0
0 0
0 10
000
00
0
0000
NA
gt-3 r
(9)
26
74
0 0
0 0
0 0
100
0000
000
00
N
A
ttl
()
36
29
12
0 1
0 22
0
453
125
0
7050
342
900
0 0
9
(7)
36
64
0 0
0 0
0 0
100
0000
000
00
N
A
CD
t
(9)
36
64
0 0
0 0
0 0
100
0000
000
00
N
A
0 ~
0 0(3
) 35
29
19
0
0 0
17
0 44
615
4
079
33
52
780
0
gt-3 r10
(7
) 35
65
0
0 0
0 0
0 10
000
00
0
0000
NA
s
(9
) 35
65
0
0 0
0 0
0 10
000
00
0
0000
NA
(3
) 44
12
20
0
0 0
24
0 21
428
6
054
37
45
450
0
15
(7)
44
56
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
44
56
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
42
9 27
0
0 0
22
0 15
517
2
113
59
55
100
0
20
(7)
42
58
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
42
58
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
53
6 19
0
0 0
22
0 12
766
0
050
21
46
340
0
30
(7)
53
47
0 0
0 0
0 0
100
0000
000
00
N
A
53
47
0 ()
0 0
0 0
100
0000
000
00
N
A
CJ
) C
J)
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w
Job
--~~
~~-
~--
Ri
Ii
w
CIl
Cl
C2
C
3 C
4
C5
C6
C7
C
8 -----------shy
(3)
51
3 21
0
1 0
24
0
61
22
4
0
46
39
456
500
40
(7
) 51
4
9
0 0
0 0
0 0
10
00
00
0
0
00
00
NA
(9
) 51
4
9
0 -----_
_
-shy
0 0
0 --shy-----~~~~
0 0
-----------shy
10
00
00
0
0
00
00
---------shy
NA
(3
) 4
8
4 33
0
0 0
15
0
769
23
0
62
20
68
75
00
50
(7
) 4
8
52
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
48
5
2
0 0
0 0
0 0
100
0000
00
00
0
N
A
51
4 3
7
0 0
0 8
0 8
16
33
05
05
8
8
22
20
0
10
0
(7)
51
49
0
0 0
0 0
0 10
000
00
0
00
00
NA
(9
) 51
4
9
0 0
0 0
0 0
100
0000
00
00
0
N
A
----------shy
(3)
56
0
35
0 0
0 9
0 0
00
00
02
29
1
79
550
0
20
0
(7)
56
4
4
0 0
0 0
0 0
100
0000
00
00
0
N
A
(9)
56
4
4
0 0
0 0
0 0
100
0000
00
00
0
N
A
69
2
24
0
0 0
5 0
64
51
6
0
13
97
82
76
00
30
0
(7)
69
31
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
(9)
69
31
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
(3)
62
1
35
0
0 0
2 0
26
31
6
0
17
96
94
59
00
~
40
0
(7)
62
3
8
0 0
0 0
0 0
10
00
00
0
0
00
00
NA
0
(9)
62
3
8
0 0
0 0
0 0
100
0000
00
00
0
N
A
fja
tJte
2-C
onat
)
gt-3
)shy z Cl
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0gtshy
Con
diti
ons
Job
E
i R
i Ii
C
l C
2 C
3 C
4 C
5 C
6 C
7 C
8 ~ rt
J gt-
lt57
0
38
0 0
0 5
0 0
0000
015
97
88
370
0
rtJ
j
500
(7)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
0 3
(9)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
tr1
tlj
0(3
) 57
1
39
0 0
0 3
0 2
3256
014
97
92
860
0
0 rtJ
60
0
(7)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
~ 57
43
0
0 0
0 0
0 10
000
00
0
0000
NA
0 0
1 34
0
gt-lt
(3)
64
0 0
1 0
277
78
0
1198
971
400
tO
3
700
(7)
64
36
0 0
0 0
0 0
100
0000
000
00
N
A
tr1 ~
(9)
64
36
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
60
2 38
0
0 0
0 0
500
00
0
0999
100
0000
800
(7)
60
40
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
60
40
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
62
0 38
0
0 0
0 0
000
00
0
0999
100
0000
900
(7)
62
38
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
62
38
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
60
0 39
0
0 0
1 0
000
0
010
99
97
500
0
1000
(7
) 60
40
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 60
40
0
0 0
0 0
0 10
000
00
0
0000
NA
w
w
(T
able
2 C
ontd
)
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------
CIJ
Con
diti
ons
CIJ
Job
E
i R
i Ii
O
J
C1
C2
C3
C4
C
5 C
6 C
7
C8
Min
44
615
4
000
00
0
0000
Sm
all
(3
) M
ax
951
220
0
8209
562
500
pro
ble
ms
Avg
66
121
6
050
01
39
021
3
_
Min
0
0000
009
99
45
450
0
Lar
ge
(3
) M
ax
214
286
1
1359
100
0000
p
rob
lem
s A
vg
605
84
0
3374
784
187
--
-_
__
---
Min
0
0000
000
00
0
0000
Ov
eral
l
(3)
Max
95
122
0
113
59
10
000
00
pro
ble
ms
Avg
26
950
0
039
40
64
715
2
tr1
()
gt-3 ~ Q
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gtshyT
able
3
z C
om
par
ativ
e re
sult
s fo
r th
e C
GP
S
CD
S
an
d N
EH
alg
ori
thm
s ~
Job 3
4
5
6
7
8
9
10
15
20
30
40
50
100
200
101
39
101
48
102
64
102
82
102
62
102
7
102
82
102
55
102
43
101
83
101
06
100
85
100
65
100
32
100
17
~CDS
100
96
101
28
102
14
102
32
102
46
102
15
102
04
102
04
101
78
101
53
100
99
100
63
100
55
100
35
100
17
~~--------------
~NElI
100
1
100
39
100
53
100
54
100
48
100
72
100
50
100
47
100
25
100
33
100
08
100
05
100
06
100
02
100
01
(Tab
le 3
Con
td)
lfl
gt-
lfl
0 tJ r t=J
0 0 i
lfl gtshy r 0 0 ~ r $
w
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to
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-----
---
co
jgt
~CGPS
~CDS
~NE
H 0
300
100
07
100
11
100
00
400
100
04
100
06
100
00
500
100
02
100
06
100
00
600
100
02
100
03
100
00
700
100
01
100
02
100
00
800
100
02
100
02
100
00
900
100
01
100
04
100
00
1000
1
0001
1
0003
1
0000
Min
1
0139
1
0096
1
0010
Sm
all
pro
ble
ms
Max
1
0282
1
0246
1
0072
Avg
1
0238
1
0192
1
0047
Min
1
0001
1
0002
1
0000
Lar
ge
pro
ble
ms
Max
1
0243
1
0178
1
0033
Avg
1
0050
1
0042
1
0005
Min
1
0001
1
0002
1
0000
r
Ov
eral
l p
rob
lem
s M
ax
102
82
102
46
100
72
0 -3A
vg
101
15
100
95
100
20
p Z
(Tab
le 3
Con
td)
0
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lgtT
able
3 (
Con
td)
Z
--------
_shy
SCG
PS
EQ
CG
PS
SCD
S SC
GP
S E
QC
GP
S SN
EH
SN
EH
EQ
IjE
H
SCD
S --
~ Jo
b C
DS
CDS
CG
PS
NEH
N
EH
CG
PS
CD
S CD
S N
EH
en
en
3 0
91
9
0
83
17
12
88
0
0 gtzj
4 2
8
7
11
2
70
28
21
76
3
gt-l
[1j
5 2
82
16
4
58
38
28
66
6
0 0 1
j6
3
77
20
1
51
48
40
56
4
en
7 5
75
20
5
43
52
45
50
5
~ 0 0
8 6
70
24
8
35
57
44
47
9
~
gt-l
9 5
6
7
28
2
40
58
46
46
8
10
7
68
25
5
40
55
42
50
8
15
7
70
23
2
45
53
46
50
4
20
8
65
27
4
46
50
37
58
5
30
8
74
18
0
57
43
35
63
2
40
7
72
21
3
53
44
29
62
9
50
13
62
25
4
49
47
31
59
10
10
0
15
6
0
25
1
52
47
27
71
2
20
0
17
64
19
2
55
43
26
68
6
30
0
12
74
14
7
63
30
17
74
9
w
(T
able
3 C
ontd
)
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Job
S
co
Ps
CD
S E
QC
GP
SC
DS
SCD
S C
GPS
SC
GP
S N
EH
E
QC
GP
SN
EH
S
NE
H
CG
PS
NE
H
SCD
S E
Q
EH
CD
S SC
DS
NE
Il
N
400
14
63
23
5
58
37
14
81
5
500
13
62
25
1
57
42
20
78
2
60
0
9
61
30
4
54
42
12
82
6
700
11
66
23
8
56
36
13
79
8
80
0
10
63
27
4
57
39
14
82
4
900
16
63
21
2
60
38
17
81
2
1000
11
61
28
4
57
39
12
82
6
Sm
all
pro
ble
ms
Min
Max
Avg
0
67
9
7
91
28
375
00
77
125
0
191
250
0
8
337
50
35
17
83
58
525
000
44
1250
12
46
347
500
46
88
598
750
0
9
537
50
Lar
ge
pro
ble
ms
Min
Max
7
17
60
74
14
30
0
8
45
63
30
53
12
46
50
82
2
10
114
00
65
3333
2
326
67
3
4000
546
000
42
0000
233
333
71
333
3
533
33
Ov
eral
l p
rob
lem
s
Min
Max
Avg
0
60
9
17
91
30
873
91
69
434
8 2
182
61
0
8
339
13
35
17
83
58
538
696
42
739
1
12
46
273
043
~~~--~--
46
0
88
10
673
478
5
3478
c 0 ~ gtshy L
0
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343 ANALYSIS OF THE CGPS ALGORITHM
average-case performance ratio the NEH algorithm is the best one folshylowed by the CDS algorithm and then by the CGPS algorithm As comshypared with the CDS algorithm in terms of the proportional superiority 87391 of problem instances better by the CGPS algorithm 694348 of problem instances equal by the CGPS and CDS algorithms and 218261 of problem instances better by the CDS algorithm on the average It follows that the CDS algorithm is better than the CGPS alshygorithm especially for the large problems Similarly the NEH algorithm is better than the CGPS algorithm and the NEH algorithm is better than the CDS algorithm especially for the small problems Therefore we have the same conclusion about these heuristics in terms of the empirishycal average-case performance ratio and proportional superiority
To sum up 503478 ofthe initial permutation schedules generated by the CGPS algorithm are the optimal schedules Among the 496522 of non-optimal schedules 647154 of the permutation schedules 1t2 improve the makespan of the initial permutation schedule However the improvement made by 1t2 is only 03940 over 1tl in terms of the empirical average-case performance ratio Moreover compared with the NEH and CDS algorithms the CGPS algorithm is the worst one in terms of the empirical average-case performance ratio and proportional superiority These imply that the CGPS algorithm is not good enough for applications
5 CONCLUSION
In this paper the 3FSS with minimum makespan criterion is considered Theoretically the heuristic algorithm proposed by Chen et a1 [2] has the best worst-case performance ratio of 53 This paper evaluates the performance of the CGPS algorithm from the empirical perspective Three simulation experiments are performed to analyze its performance in terms of the six evaluation measures on a total of 13800 problems Four conclusions can be drawn from this paper Firstly in the CGPS algorithm 503478 of the initial permutation schedules are the optimal ones 321326 of the permutation schedshyules 1t2 improve the makespan of nl and 175196 of the permutation schedules n2 do not improve their makespan Secondly the empirical average-case performance ratio of initial permutation schedule genershyated by the CGPS algorithm is always smaller than the performance guarantee 53 developed by Chen et al [2] Its empirical average-case performance ratio is in the range of 10011 and 10355 and is 10156 on the average Thirdly the permutation schedule 1t2 improves 1tl in terms of the empirical average-case performance ratio by the non-significant percentage 03940 Fourthly among the three heuristic algorithms the
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344 H C TANG
NEH algorithm is the best one in terms of both empirical average-case performance ratio and proportional superiority followed by the CDS alshygorithm and then by the CGPS algorithm Consequently the CGPS algoshyrithm is not suitable for the 3FSS with minimum makespan criterion
REFERENCES
l H G Campbell R A Dudek and M L Smith A heuristic algorithm for the n-job tn-machine sequencing problem Management Science Vol 16 (1970) pp 630-637
2 B Chen C A Glass C N Potts and V A Strusevich A new heuristic for threeshymachine flow shop scheduling Operations Research Vol 44 (1996) pp 891-898
3 R W Conway W L Maxwell and L W Miller Theory of Scheduling AddisonshyWesley Reading MA 1967
4 R A Dudek S S Panwalkar and M L Smith The lessons of flowshop scheduling research Operations Research Vol 40 (1992) pp 7-13
5 M R Garey D S Johnson and R Sethi The complexity of flowshop and jobshop scheduling Mathematics Operations Research Vol 1 (1976) pp 117-129
6 S M Johnson Optimal two- and three-stage production schedules with setup times included Naval Research Logistic Quarterly Vol 1 (1954) pp 61-68
7 B J Lageweg J K Lenstra and A H G Rinnooy Kan A general bounding scheme for the permutation flow-shop problem Operations Research Vol 26 (1978) pp 53-67
8 E L Lawler J K Lenstra A H G Rinnooy Kan and D B Shmoys Sequencing and Scheduling Algorithms and Complexity in S C Graves A H G Rinnooy
-Kan and P H Zipkin Handbooks in Operations Research and Management Science Vol 4 Logistics of Production and Inventory North Holland Amsterdam 1993 pp 445-522
9 J K Lenstra A H G Rinnooy Kan and P Brucker Complexity of machine scheduling problems Annals Discrete Mathematics Vol 1 (1977) pp 343-362
10 M Nawaz E E Enscore Jr and I Ham A heuristic algorithm for the m-machine n-job flow-shop sequencing problem OMEGA Vol 11 (1983) pp 91-95
II C N Potts An Adaptive branching rule for the permutation flow-shop problem European Journal of Operational Research Vol 5 (1980) pp 19-25
12 A H G Rinnooy Kan Machine scheduling problems classification complexity and computations Nijhoff The Hague 1976
13 H Rock and G Schmidt Machine aggregation heuristics in shop scheduling Methods of Operations Research Vol 45 (1983) pp 303-314
14 C Smutnicki Some results of the worst-case analysis for flow shop scheduling European Journal of Operational Research Vol 109 (1998) pp 66-87
15 S M A Suliman A two-phase heuristic approach to the permutation flow-shop scheduling problem International Journal of Production Economics Vol 64 (2000) pp 143-152
16 H C Tang A new lower bonding rule for permutation flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 249-257
17 H C Tang A modification to the CGPS algorithm for three-machine flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 321-331
Received January 2001
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328 H C TANG
It is recognized that the lower bound (8) considers all values of v in (2) but the lower bound (6) only considers two of its values This implies that the lower bound (8) is stronger than (6) Therefore condition (9) is less restrictive than condition (7) This result is stated formally below
LEMMA 5 If condition (7) holds then (9) holds
Therefore among the three conditions condition (9) is the least restrictive from Lemma 4 and Lemma 5
The second notable observation is that the schedule 1t2 does not always improve the makespan of the schedule 1tl In other words the makespan of1t2 sometimes is larger than or equal to the makespan Of1tl
Taking into consideration the above pieces of information our conclusion is that eight cases arise through the various possible comshybinations For condition (3) ~s an example these cases are
(C1) 11 =v
(C2) 11 lt v and (3) holds
(C3) 11 lt v (3) is violated Cmai1tl) gt Cmai1t0 and (4) holds
(C4) 11 lt v (3) is violated Cmai1tl) gt Cmai1t2) and (4) is violated (10)
(C5) 11 lt v (3) is violated Cmai1tl) = Cmai1t2) and (4) holds
(C6) 11 lt v (3) is violated Cmai1tl) = Cmai1t0 and (4) is violated
(C7) 11 lt v (3) is violated Cmai1tl) lt Cmai1t2) and (4) holds
(C8) 11 lt v (3) is violated Cmai1tl) lt Cmai1t2) and (4) is violated
Similarly for other two proposed conditions we replace (3) by (7) and (9) respectively
3 EVALUATION MEASURES
To evaluate the performance of the CGPS algorithm this paper provides a number of evaluation measures in three respects The first respect refers to the effectiveness of the CGPS algorithm The second respect is concerned with the comparison of the three conditions satisfyshying the desired performance (4) The final respect is apout comparing with the heuristics developed by Nawaz Enscore and Ham (NEH) [10] and by Campbell Dudek and Smith (CDS) [1] which are the best two among the constructive algorithms for the FSS with minimum makespan criterion
Firstly to measure the effectiveness of the CGPS algorithm two evaluation measures are adopted In Lemma 2 for the case that
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329 ANALYSIS OF THE CGPS ALGORITHM
l == v an optimal schedule is found from the CGPS algorithm It follows that the percentage of schedules that are equal to the optimal ones is used to measure its effectiveness This proportional optimum is defined as
P [(C1)Tl 100 (11)
where (C1) denotes the number of times the schedule Xl that is equal to an optimal one in T problem instances Another evaluation measshyure is the empirical average-case performance ratio For the algorithm A and problem instance Z its quality is measured by the ratio
(12)
It is recognized that the worst-case performance ratio provides a performance guarantee which is valid for all problem instances As stated in Chen et al [2] the CGPS algorithm is by far the best one with the worst-case performance ratio as the performance measure However the Operations Research practitioners also concern the comshyputational experience of the CGPS algorithm Thus we adopt the empirical average-case performance ratio
T
~A L llA(Zi)T (13) i=l
to measure its performance empirically For notational convenience the argument Z will be omitted in Cmaix Z) and Cmax(x Z) if it is not necessary in the following
Secondly to compare conditions (3) (7) and (9) satisfying the deshysired performance (4) three evaluation measures are proposed We only consider condition (3) since the other conditions are treated simishylarly It is recognized that the desired performance (4) sometimes holds but condition (3) violates This implies that the percentage of equivalent relation between condition (3) and the desired performance (4) can be used as an evaluation measure This proportional equivashylency can be defined as
i _[ (C2) + (C4) +(C6) + (C8) ] E - (C2)+(C3)+(C4)+(C5)+(C6)+(C7)+(C8) 100
for i == 3 7 9 (14)
where (C2) + (C4) + (C6) + (C8) is thee number of times the equivalent relation between the desired performance (4) and condition (3) The other evaluation measures concern an improvement on the makespan of the schedule Xl Two evaluation measures are distinshy
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330 HCTANG
guished One is the relative error of the schedule 1tl from the schedule 1tH in terms of the 11Z)
i [ Cmax(1tH Z)Cmax(1t Z) 1R 1- 100 Cmax(1tl Z)CmaxC1t Z)
Cmax(1tH Z) ] (15) 1 - C ( l Z) 100 for pound 3 7 9[ 1tmax
The other is an improvement on the makespan of the schedule 1t1 in terms of the problem instance The definition of this proportional imshyprovement is
Ii _ [ (C3) + (C4) ] - (C3) +(C4) +(C5) +(C6) +(C7) +(C8) 100
for i 3 7 9 (16)
where (C3) + (C4) is the number of times the schedule 1tH that imshyproves the makespan of the schedule 1tl
Finally in concern with the comparison of the CGPS NEH and CDS algorithms two evaluation measures are adopted One measure is based on the empirical average-case performance ratio (13) The other measure is that the percentage of problem instances that the A algorithm is superior to the B algorithm in its makespan This proporshytional superiority can be defined as
A _[ (A) ] (17)SB - (A) +(AB) + (B) 100
where (A) and (B) are the number of times the makespan better by A and B algorithms respectively and (AB) is the number of times the makespan equal by A and B algorithms Denote E~ the percentage of problem instances that the A algorithm is equal to the B algorithm
Therefore there are six evaluation measures adopted in this paper We use (11) and (13) to measure the effectiveness of the CGPS algoshyrithm To compare conditions (3) (7) and (9) satisfying the desired pershyformance (4) three evaluation measures (14) (15) and (16) are adopted To compare the CGPS algorithm with the NEH algorithm as well as the CDS algorithm two evaluation measures (13) and (17) are used
4 SIMULATION EXPERIMENTS
To get an idea of the CGPS algorithm from the empirical perspecshytive three simulation experiments are conducted in this paper The first simulation experiment examines the effectiveness of the CGPS algoshy
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331 ANALYSIS OF THE CGPS ALGORITHM
rithm The second simulation experiment concerns the empirical comparishyson of conditions (3) (7) and (9) which are the sufficient for the desired performance (4) The third simulation experiment conducts the comparison of the CGPS algorithm with the NEH and CDS algorithms
The number of machines considered is three and the numbers of jobs considered are 3 4 5 6 7 8 9 10 15 20 30 40 50 100 200 300 400 500 600 700 800 900 and 1000 The problem size is dishyvided into two classes One is the small problem for the 3 4 5 6 7 8 9 and 10 job problems The other is the large problem for the 15 20 30 40 50 100 200 300 400 500 600 700 800 900 and 1000 job problems One hundred problems are generated for each job The processing times for each problem are randomly generated integers uniformly distributed between 1 and 100 A more portable random number generator Xn == 16807xH(mod 231
- 1) is adopted using same to generate the random values for each problem The whole simushy
lation experiments are subjected to extensive testing 13800 problems A number of evaluation measures described in previous section
are used for the three simulation experiments We adopt the evaluation measures (11) and (13) for the first simulation experiment (14) (15) and (16) for the second simulation experiment (13) and (17) for the third simulation experiment respectively The whole computation is conducted on a 266 MHz Pentinum II PC using Microsoft Visual C++ compiler under Microsoft Windows 98 operating system
To obtain an optimal schedule for the empirical average-case pershyformance ratio the branch-and-bound algorithm with two-machine lower bound rule proposed by Tang [16] is applied This proposed twoshymachine lower bound is a modification to the algorithms of Lageweg et al [7] and Potts [11] and is shown to be more efficient Whenever a problem is not solved after 100000 nodes have been generated comshyputation is stopped In this case the optimal makespan is replaced by the best-known makespan Thus the empirical average-case pershyformance ratio is underestimated
The first simulation experiment is concerned with the effectiveness of the CGPS algorithm Detailed results are presented in Table 1 where Min denotes the minimum value of proportional optimum or empirical average-case performance ratio Max and Avg the corresshyponding maximum and average values respectively The proportional optimum is 503478 on the average In other words 503478 of the initial permutation schedules generated by the CGPS algorithm are the optimal schedules especially for the large problems the empirical average-case performance ratio is concerned Table 1 lists the percentage POP of problem instances that the optimal schedules are found by the
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332 H C TANG
branch-and-bound algorithm with Tangs two-machine lower bound rule [16] The average percentage Popt is 962609 Especially in the case of small problems the branch-and-bound algorithm can always find the optimal schedules within 100000 nodes generated Therefore it is appropriate to adopt the empirical average-ca~e performance ratio to measure the effectiveness of the CGPS algorithm The empirical average-case performance ratio of the initial permutation schedule is in the range of 10011 and 10355 and is 10156 on the average While the empirical average-case performance ratio of the CGPS algorithm is in the range of 10001 and 10282 and is 10115 on the average These values are always smaller than the performance guarantee 53 of the CGPS algorithm
Table 1 Results of simulations for the effectiveness of the COPS
Job P Popt ~1t1
3 59 100 10139 4 53 100 10185 10148 5 40 100 10309 10264 6 40 100 10317 10282 7 33 100 10318 10262 8 26 100 10355 10270 9 36 100 10355 10282 10 35 100 10337 10255 15 44 84 10299 10243 20 42 82 10300 10183 30 53 90 10157 10106 40 51 94 10132 10085 50 48 95 10128 10065 100 51 93 10083 10032 200 56 97 10040 10017 300 69 99 10021 10007 400 62 98 10022 10004 500 57 96 10018 10002 600 57 99 10017 10002 700 64 96 10013 10001 800 60 99 10012 10002
(Table 1 Contd)
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333 ANALYSIS OF THE CGPS ALGORITHM
lOOO 60 97 10001
Small problems Max
Avg 59
402500 lOO lOO
10355 10289
10139 10282 10238
Large problems
Min Max
42 82 10011 10300
10001 10243 10050
Overall problems Max 69
503478 100
922609 10355 10156
10001 10282 10115
The second experiment refers to the comparison of conditions (3) (7) and (9) in terms of the proportional equivalency (14) the relative error (15) and the proportional improvement (16) Table 2 gives the reshysults Notably the number of times of the cases C3 C4 C5 C6 C7 and C8 for conditions (7) and (9) are all zeros In other words the CGPS algorithm with condition (7) or (9) only generates the initial permutashytion schedule It follows that the proportional equivalency is lOO the relative error is zero and the proportional improvement is not availshyable (NA) for each problem These results indicate that the initial pershymutation schedule never violates the worst-case performance ratio of 53 if we adopt the CGPS algorithm with condition (7) or (9)
In the case of condition (3) viz the original CGPS algorithm the proportional equivalency is 2695 on the average This value indicates that 7305 of the desired performance (4) holds also when condition (3) is violated Therefore condition (3) is too restrictive especially for the large problems In concern with the improvement on the makesshypan of 1tl the relative error is 03940 and the proportional improveshyment is 647152 on the average This implies that 647154 of the permutation schedule 1t2 has improved on the initial permutation schedule in its makespan However the quality of improvement made by 1t2 over 1tl is only 03940 in terms of the empirical average-case performance ratio Therefore condition (3) in the CGPS algorithm is more restrictive and the improvement in the average-case performance ratio is so marginal from the empirical point of view
The third experiment concerns the comparison of the CGPS algoshyrithm with the NEH algorithm as well as the CDS algorithm Comparashytive results are shown in Table 3 On basis of the empirical
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ltgtl
ltgtl
Tab
le 2
C
om
para
tiv
e r
esu
lts
for
co
nd
itio
ns
(3)
(7)
an
d (
9)
--------------shy
Job
C
ondi
tion
s E
i R
i Ii
C
1 C
2 C
3 C
4 C
5 C
6 C
7 C
8 -----shy
(3)
59
39
0 0
2 0
0 0
951
220
0
0000
000
00
3 (7
) 59
41
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 59
41
0
0 0
0 0
0 10
000
00
0
0000
NA
-shy
(3)
53
39
4 0
0 0
4 0
829
787
0
3633
500
000
4 (7
) 53
47
0
0 0
0 0
0 10
000
00
0
0000
NA
53
47
0
0 0
0 0
0 10
000
00
0
0000
NA
---shy
-----------shy
(3)
40
44
9 0
1 0
6 0
733
333
0
4365
562
500
5 (7
) 40
60
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 40
eo
0
0 0
0 0
0 10
000
00
0
0000
NA
(3
) 40
4
7
5 0
0 0
8 0
783
333
0
3392
384
600
6 (7
) 40
6
0
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
40
60
0
0 0
0 0
0 10
000
00
0
000
0
NA
-----------shy
(3)
33
37
10
0
0 0
20
0 55
223
9
054
27
33
330
0
7 (7
) 33
6
7
0 0
0 0
0 0
100
0000
000
00
N
A
p (9
) 33
6
7
0 0
0 0
0 0
100
0000
000
00
N
A
0 ------~
(Tab
le 2
Con
td)
gtshy Z
C
)
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Con
diti
ons
~ z
Job
E
i R
i Ii
gt
C
l C
2 C
3 C
4 C
5 C
6 C
7 C
8 S CD
26
40
16
0 1
0 17
0
540
541
0
8209
470
600
CD
0 gt1j
8 (7
) 26
74
0
0 0
0 0
0 10
000
00
0
0000
NA
gt-3 r
(9)
26
74
0 0
0 0
0 0
100
0000
000
00
N
A
ttl
()
36
29
12
0 1
0 22
0
453
125
0
7050
342
900
0 0
9
(7)
36
64
0 0
0 0
0 0
100
0000
000
00
N
A
CD
t
(9)
36
64
0 0
0 0
0 0
100
0000
000
00
N
A
0 ~
0 0(3
) 35
29
19
0
0 0
17
0 44
615
4
079
33
52
780
0
gt-3 r10
(7
) 35
65
0
0 0
0 0
0 10
000
00
0
0000
NA
s
(9
) 35
65
0
0 0
0 0
0 10
000
00
0
0000
NA
(3
) 44
12
20
0
0 0
24
0 21
428
6
054
37
45
450
0
15
(7)
44
56
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
44
56
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
42
9 27
0
0 0
22
0 15
517
2
113
59
55
100
0
20
(7)
42
58
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
42
58
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
53
6 19
0
0 0
22
0 12
766
0
050
21
46
340
0
30
(7)
53
47
0 0
0 0
0 0
100
0000
000
00
N
A
53
47
0 ()
0 0
0 0
100
0000
000
00
N
A
CJ
) C
J)
Of
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w
Job
--~~
~~-
~--
Ri
Ii
w
CIl
Cl
C2
C
3 C
4
C5
C6
C7
C
8 -----------shy
(3)
51
3 21
0
1 0
24
0
61
22
4
0
46
39
456
500
40
(7
) 51
4
9
0 0
0 0
0 0
10
00
00
0
0
00
00
NA
(9
) 51
4
9
0 -----_
_
-shy
0 0
0 --shy-----~~~~
0 0
-----------shy
10
00
00
0
0
00
00
---------shy
NA
(3
) 4
8
4 33
0
0 0
15
0
769
23
0
62
20
68
75
00
50
(7
) 4
8
52
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
48
5
2
0 0
0 0
0 0
100
0000
00
00
0
N
A
51
4 3
7
0 0
0 8
0 8
16
33
05
05
8
8
22
20
0
10
0
(7)
51
49
0
0 0
0 0
0 10
000
00
0
00
00
NA
(9
) 51
4
9
0 0
0 0
0 0
100
0000
00
00
0
N
A
----------shy
(3)
56
0
35
0 0
0 9
0 0
00
00
02
29
1
79
550
0
20
0
(7)
56
4
4
0 0
0 0
0 0
100
0000
00
00
0
N
A
(9)
56
4
4
0 0
0 0
0 0
100
0000
00
00
0
N
A
69
2
24
0
0 0
5 0
64
51
6
0
13
97
82
76
00
30
0
(7)
69
31
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
(9)
69
31
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
(3)
62
1
35
0
0 0
2 0
26
31
6
0
17
96
94
59
00
~
40
0
(7)
62
3
8
0 0
0 0
0 0
10
00
00
0
0
00
00
NA
0
(9)
62
3
8
0 0
0 0
0 0
100
0000
00
00
0
N
A
fja
tJte
2-C
onat
)
gt-3
)shy z Cl
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0gtshy
Con
diti
ons
Job
E
i R
i Ii
C
l C
2 C
3 C
4 C
5 C
6 C
7 C
8 ~ rt
J gt-
lt57
0
38
0 0
0 5
0 0
0000
015
97
88
370
0
rtJ
j
500
(7)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
0 3
(9)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
tr1
tlj
0(3
) 57
1
39
0 0
0 3
0 2
3256
014
97
92
860
0
0 rtJ
60
0
(7)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
~ 57
43
0
0 0
0 0
0 10
000
00
0
0000
NA
0 0
1 34
0
gt-lt
(3)
64
0 0
1 0
277
78
0
1198
971
400
tO
3
700
(7)
64
36
0 0
0 0
0 0
100
0000
000
00
N
A
tr1 ~
(9)
64
36
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
60
2 38
0
0 0
0 0
500
00
0
0999
100
0000
800
(7)
60
40
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
60
40
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
62
0 38
0
0 0
0 0
000
00
0
0999
100
0000
900
(7)
62
38
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
62
38
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
60
0 39
0
0 0
1 0
000
0
010
99
97
500
0
1000
(7
) 60
40
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 60
40
0
0 0
0 0
0 10
000
00
0
0000
NA
w
w
(T
able
2 C
ontd
)
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------
CIJ
Con
diti
ons
CIJ
Job
E
i R
i Ii
O
J
C1
C2
C3
C4
C
5 C
6 C
7
C8
Min
44
615
4
000
00
0
0000
Sm
all
(3
) M
ax
951
220
0
8209
562
500
pro
ble
ms
Avg
66
121
6
050
01
39
021
3
_
Min
0
0000
009
99
45
450
0
Lar
ge
(3
) M
ax
214
286
1
1359
100
0000
p
rob
lem
s A
vg
605
84
0
3374
784
187
--
-_
__
---
Min
0
0000
000
00
0
0000
Ov
eral
l
(3)
Max
95
122
0
113
59
10
000
00
pro
ble
ms
Avg
26
950
0
039
40
64
715
2
tr1
()
gt-3 ~ Q
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gtshyT
able
3
z C
om
par
ativ
e re
sult
s fo
r th
e C
GP
S
CD
S
an
d N
EH
alg
ori
thm
s ~
Job 3
4
5
6
7
8
9
10
15
20
30
40
50
100
200
101
39
101
48
102
64
102
82
102
62
102
7
102
82
102
55
102
43
101
83
101
06
100
85
100
65
100
32
100
17
~CDS
100
96
101
28
102
14
102
32
102
46
102
15
102
04
102
04
101
78
101
53
100
99
100
63
100
55
100
35
100
17
~~--------------
~NElI
100
1
100
39
100
53
100
54
100
48
100
72
100
50
100
47
100
25
100
33
100
08
100
05
100
06
100
02
100
01
(Tab
le 3
Con
td)
lfl
gt-
lfl
0 tJ r t=J
0 0 i
lfl gtshy r 0 0 ~ r $
w
w
to
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-----
---
co
jgt
~CGPS
~CDS
~NE
H 0
300
100
07
100
11
100
00
400
100
04
100
06
100
00
500
100
02
100
06
100
00
600
100
02
100
03
100
00
700
100
01
100
02
100
00
800
100
02
100
02
100
00
900
100
01
100
04
100
00
1000
1
0001
1
0003
1
0000
Min
1
0139
1
0096
1
0010
Sm
all
pro
ble
ms
Max
1
0282
1
0246
1
0072
Avg
1
0238
1
0192
1
0047
Min
1
0001
1
0002
1
0000
Lar
ge
pro
ble
ms
Max
1
0243
1
0178
1
0033
Avg
1
0050
1
0042
1
0005
Min
1
0001
1
0002
1
0000
r
Ov
eral
l p
rob
lem
s M
ax
102
82
102
46
100
72
0 -3A
vg
101
15
100
95
100
20
p Z
(Tab
le 3
Con
td)
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lgtT
able
3 (
Con
td)
Z
--------
_shy
SCG
PS
EQ
CG
PS
SCD
S SC
GP
S E
QC
GP
S SN
EH
SN
EH
EQ
IjE
H
SCD
S --
~ Jo
b C
DS
CDS
CG
PS
NEH
N
EH
CG
PS
CD
S CD
S N
EH
en
en
3 0
91
9
0
83
17
12
88
0
0 gtzj
4 2
8
7
11
2
70
28
21
76
3
gt-l
[1j
5 2
82
16
4
58
38
28
66
6
0 0 1
j6
3
77
20
1
51
48
40
56
4
en
7 5
75
20
5
43
52
45
50
5
~ 0 0
8 6
70
24
8
35
57
44
47
9
~
gt-l
9 5
6
7
28
2
40
58
46
46
8
10
7
68
25
5
40
55
42
50
8
15
7
70
23
2
45
53
46
50
4
20
8
65
27
4
46
50
37
58
5
30
8
74
18
0
57
43
35
63
2
40
7
72
21
3
53
44
29
62
9
50
13
62
25
4
49
47
31
59
10
10
0
15
6
0
25
1
52
47
27
71
2
20
0
17
64
19
2
55
43
26
68
6
30
0
12
74
14
7
63
30
17
74
9
w
(T
able
3 C
ontd
)
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Job
S
co
Ps
CD
S E
QC
GP
SC
DS
SCD
S C
GPS
SC
GP
S N
EH
E
QC
GP
SN
EH
S
NE
H
CG
PS
NE
H
SCD
S E
Q
EH
CD
S SC
DS
NE
Il
N
400
14
63
23
5
58
37
14
81
5
500
13
62
25
1
57
42
20
78
2
60
0
9
61
30
4
54
42
12
82
6
700
11
66
23
8
56
36
13
79
8
80
0
10
63
27
4
57
39
14
82
4
900
16
63
21
2
60
38
17
81
2
1000
11
61
28
4
57
39
12
82
6
Sm
all
pro
ble
ms
Min
Max
Avg
0
67
9
7
91
28
375
00
77
125
0
191
250
0
8
337
50
35
17
83
58
525
000
44
1250
12
46
347
500
46
88
598
750
0
9
537
50
Lar
ge
pro
ble
ms
Min
Max
7
17
60
74
14
30
0
8
45
63
30
53
12
46
50
82
2
10
114
00
65
3333
2
326
67
3
4000
546
000
42
0000
233
333
71
333
3
533
33
Ov
eral
l p
rob
lem
s
Min
Max
Avg
0
60
9
17
91
30
873
91
69
434
8 2
182
61
0
8
339
13
35
17
83
58
538
696
42
739
1
12
46
273
043
~~~--~--
46
0
88
10
673
478
5
3478
c 0 ~ gtshy L
0
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343 ANALYSIS OF THE CGPS ALGORITHM
average-case performance ratio the NEH algorithm is the best one folshylowed by the CDS algorithm and then by the CGPS algorithm As comshypared with the CDS algorithm in terms of the proportional superiority 87391 of problem instances better by the CGPS algorithm 694348 of problem instances equal by the CGPS and CDS algorithms and 218261 of problem instances better by the CDS algorithm on the average It follows that the CDS algorithm is better than the CGPS alshygorithm especially for the large problems Similarly the NEH algorithm is better than the CGPS algorithm and the NEH algorithm is better than the CDS algorithm especially for the small problems Therefore we have the same conclusion about these heuristics in terms of the empirishycal average-case performance ratio and proportional superiority
To sum up 503478 ofthe initial permutation schedules generated by the CGPS algorithm are the optimal schedules Among the 496522 of non-optimal schedules 647154 of the permutation schedules 1t2 improve the makespan of the initial permutation schedule However the improvement made by 1t2 is only 03940 over 1tl in terms of the empirical average-case performance ratio Moreover compared with the NEH and CDS algorithms the CGPS algorithm is the worst one in terms of the empirical average-case performance ratio and proportional superiority These imply that the CGPS algorithm is not good enough for applications
5 CONCLUSION
In this paper the 3FSS with minimum makespan criterion is considered Theoretically the heuristic algorithm proposed by Chen et a1 [2] has the best worst-case performance ratio of 53 This paper evaluates the performance of the CGPS algorithm from the empirical perspective Three simulation experiments are performed to analyze its performance in terms of the six evaluation measures on a total of 13800 problems Four conclusions can be drawn from this paper Firstly in the CGPS algorithm 503478 of the initial permutation schedules are the optimal ones 321326 of the permutation schedshyules 1t2 improve the makespan of nl and 175196 of the permutation schedules n2 do not improve their makespan Secondly the empirical average-case performance ratio of initial permutation schedule genershyated by the CGPS algorithm is always smaller than the performance guarantee 53 developed by Chen et al [2] Its empirical average-case performance ratio is in the range of 10011 and 10355 and is 10156 on the average Thirdly the permutation schedule 1t2 improves 1tl in terms of the empirical average-case performance ratio by the non-significant percentage 03940 Fourthly among the three heuristic algorithms the
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344 H C TANG
NEH algorithm is the best one in terms of both empirical average-case performance ratio and proportional superiority followed by the CDS alshygorithm and then by the CGPS algorithm Consequently the CGPS algoshyrithm is not suitable for the 3FSS with minimum makespan criterion
REFERENCES
l H G Campbell R A Dudek and M L Smith A heuristic algorithm for the n-job tn-machine sequencing problem Management Science Vol 16 (1970) pp 630-637
2 B Chen C A Glass C N Potts and V A Strusevich A new heuristic for threeshymachine flow shop scheduling Operations Research Vol 44 (1996) pp 891-898
3 R W Conway W L Maxwell and L W Miller Theory of Scheduling AddisonshyWesley Reading MA 1967
4 R A Dudek S S Panwalkar and M L Smith The lessons of flowshop scheduling research Operations Research Vol 40 (1992) pp 7-13
5 M R Garey D S Johnson and R Sethi The complexity of flowshop and jobshop scheduling Mathematics Operations Research Vol 1 (1976) pp 117-129
6 S M Johnson Optimal two- and three-stage production schedules with setup times included Naval Research Logistic Quarterly Vol 1 (1954) pp 61-68
7 B J Lageweg J K Lenstra and A H G Rinnooy Kan A general bounding scheme for the permutation flow-shop problem Operations Research Vol 26 (1978) pp 53-67
8 E L Lawler J K Lenstra A H G Rinnooy Kan and D B Shmoys Sequencing and Scheduling Algorithms and Complexity in S C Graves A H G Rinnooy
-Kan and P H Zipkin Handbooks in Operations Research and Management Science Vol 4 Logistics of Production and Inventory North Holland Amsterdam 1993 pp 445-522
9 J K Lenstra A H G Rinnooy Kan and P Brucker Complexity of machine scheduling problems Annals Discrete Mathematics Vol 1 (1977) pp 343-362
10 M Nawaz E E Enscore Jr and I Ham A heuristic algorithm for the m-machine n-job flow-shop sequencing problem OMEGA Vol 11 (1983) pp 91-95
II C N Potts An Adaptive branching rule for the permutation flow-shop problem European Journal of Operational Research Vol 5 (1980) pp 19-25
12 A H G Rinnooy Kan Machine scheduling problems classification complexity and computations Nijhoff The Hague 1976
13 H Rock and G Schmidt Machine aggregation heuristics in shop scheduling Methods of Operations Research Vol 45 (1983) pp 303-314
14 C Smutnicki Some results of the worst-case analysis for flow shop scheduling European Journal of Operational Research Vol 109 (1998) pp 66-87
15 S M A Suliman A two-phase heuristic approach to the permutation flow-shop scheduling problem International Journal of Production Economics Vol 64 (2000) pp 143-152
16 H C Tang A new lower bonding rule for permutation flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 249-257
17 H C Tang A modification to the CGPS algorithm for three-machine flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 321-331
Received January 2001
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329 ANALYSIS OF THE CGPS ALGORITHM
l == v an optimal schedule is found from the CGPS algorithm It follows that the percentage of schedules that are equal to the optimal ones is used to measure its effectiveness This proportional optimum is defined as
P [(C1)Tl 100 (11)
where (C1) denotes the number of times the schedule Xl that is equal to an optimal one in T problem instances Another evaluation measshyure is the empirical average-case performance ratio For the algorithm A and problem instance Z its quality is measured by the ratio
(12)
It is recognized that the worst-case performance ratio provides a performance guarantee which is valid for all problem instances As stated in Chen et al [2] the CGPS algorithm is by far the best one with the worst-case performance ratio as the performance measure However the Operations Research practitioners also concern the comshyputational experience of the CGPS algorithm Thus we adopt the empirical average-case performance ratio
T
~A L llA(Zi)T (13) i=l
to measure its performance empirically For notational convenience the argument Z will be omitted in Cmaix Z) and Cmax(x Z) if it is not necessary in the following
Secondly to compare conditions (3) (7) and (9) satisfying the deshysired performance (4) three evaluation measures are proposed We only consider condition (3) since the other conditions are treated simishylarly It is recognized that the desired performance (4) sometimes holds but condition (3) violates This implies that the percentage of equivalent relation between condition (3) and the desired performance (4) can be used as an evaluation measure This proportional equivashylency can be defined as
i _[ (C2) + (C4) +(C6) + (C8) ] E - (C2)+(C3)+(C4)+(C5)+(C6)+(C7)+(C8) 100
for i == 3 7 9 (14)
where (C2) + (C4) + (C6) + (C8) is thee number of times the equivalent relation between the desired performance (4) and condition (3) The other evaluation measures concern an improvement on the makespan of the schedule Xl Two evaluation measures are distinshy
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330 HCTANG
guished One is the relative error of the schedule 1tl from the schedule 1tH in terms of the 11Z)
i [ Cmax(1tH Z)Cmax(1t Z) 1R 1- 100 Cmax(1tl Z)CmaxC1t Z)
Cmax(1tH Z) ] (15) 1 - C ( l Z) 100 for pound 3 7 9[ 1tmax
The other is an improvement on the makespan of the schedule 1t1 in terms of the problem instance The definition of this proportional imshyprovement is
Ii _ [ (C3) + (C4) ] - (C3) +(C4) +(C5) +(C6) +(C7) +(C8) 100
for i 3 7 9 (16)
where (C3) + (C4) is the number of times the schedule 1tH that imshyproves the makespan of the schedule 1tl
Finally in concern with the comparison of the CGPS NEH and CDS algorithms two evaluation measures are adopted One measure is based on the empirical average-case performance ratio (13) The other measure is that the percentage of problem instances that the A algorithm is superior to the B algorithm in its makespan This proporshytional superiority can be defined as
A _[ (A) ] (17)SB - (A) +(AB) + (B) 100
where (A) and (B) are the number of times the makespan better by A and B algorithms respectively and (AB) is the number of times the makespan equal by A and B algorithms Denote E~ the percentage of problem instances that the A algorithm is equal to the B algorithm
Therefore there are six evaluation measures adopted in this paper We use (11) and (13) to measure the effectiveness of the CGPS algoshyrithm To compare conditions (3) (7) and (9) satisfying the desired pershyformance (4) three evaluation measures (14) (15) and (16) are adopted To compare the CGPS algorithm with the NEH algorithm as well as the CDS algorithm two evaluation measures (13) and (17) are used
4 SIMULATION EXPERIMENTS
To get an idea of the CGPS algorithm from the empirical perspecshytive three simulation experiments are conducted in this paper The first simulation experiment examines the effectiveness of the CGPS algoshy
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331 ANALYSIS OF THE CGPS ALGORITHM
rithm The second simulation experiment concerns the empirical comparishyson of conditions (3) (7) and (9) which are the sufficient for the desired performance (4) The third simulation experiment conducts the comparison of the CGPS algorithm with the NEH and CDS algorithms
The number of machines considered is three and the numbers of jobs considered are 3 4 5 6 7 8 9 10 15 20 30 40 50 100 200 300 400 500 600 700 800 900 and 1000 The problem size is dishyvided into two classes One is the small problem for the 3 4 5 6 7 8 9 and 10 job problems The other is the large problem for the 15 20 30 40 50 100 200 300 400 500 600 700 800 900 and 1000 job problems One hundred problems are generated for each job The processing times for each problem are randomly generated integers uniformly distributed between 1 and 100 A more portable random number generator Xn == 16807xH(mod 231
- 1) is adopted using same to generate the random values for each problem The whole simushy
lation experiments are subjected to extensive testing 13800 problems A number of evaluation measures described in previous section
are used for the three simulation experiments We adopt the evaluation measures (11) and (13) for the first simulation experiment (14) (15) and (16) for the second simulation experiment (13) and (17) for the third simulation experiment respectively The whole computation is conducted on a 266 MHz Pentinum II PC using Microsoft Visual C++ compiler under Microsoft Windows 98 operating system
To obtain an optimal schedule for the empirical average-case pershyformance ratio the branch-and-bound algorithm with two-machine lower bound rule proposed by Tang [16] is applied This proposed twoshymachine lower bound is a modification to the algorithms of Lageweg et al [7] and Potts [11] and is shown to be more efficient Whenever a problem is not solved after 100000 nodes have been generated comshyputation is stopped In this case the optimal makespan is replaced by the best-known makespan Thus the empirical average-case pershyformance ratio is underestimated
The first simulation experiment is concerned with the effectiveness of the CGPS algorithm Detailed results are presented in Table 1 where Min denotes the minimum value of proportional optimum or empirical average-case performance ratio Max and Avg the corresshyponding maximum and average values respectively The proportional optimum is 503478 on the average In other words 503478 of the initial permutation schedules generated by the CGPS algorithm are the optimal schedules especially for the large problems the empirical average-case performance ratio is concerned Table 1 lists the percentage POP of problem instances that the optimal schedules are found by the
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332 H C TANG
branch-and-bound algorithm with Tangs two-machine lower bound rule [16] The average percentage Popt is 962609 Especially in the case of small problems the branch-and-bound algorithm can always find the optimal schedules within 100000 nodes generated Therefore it is appropriate to adopt the empirical average-ca~e performance ratio to measure the effectiveness of the CGPS algorithm The empirical average-case performance ratio of the initial permutation schedule is in the range of 10011 and 10355 and is 10156 on the average While the empirical average-case performance ratio of the CGPS algorithm is in the range of 10001 and 10282 and is 10115 on the average These values are always smaller than the performance guarantee 53 of the CGPS algorithm
Table 1 Results of simulations for the effectiveness of the COPS
Job P Popt ~1t1
3 59 100 10139 4 53 100 10185 10148 5 40 100 10309 10264 6 40 100 10317 10282 7 33 100 10318 10262 8 26 100 10355 10270 9 36 100 10355 10282 10 35 100 10337 10255 15 44 84 10299 10243 20 42 82 10300 10183 30 53 90 10157 10106 40 51 94 10132 10085 50 48 95 10128 10065 100 51 93 10083 10032 200 56 97 10040 10017 300 69 99 10021 10007 400 62 98 10022 10004 500 57 96 10018 10002 600 57 99 10017 10002 700 64 96 10013 10001 800 60 99 10012 10002
(Table 1 Contd)
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333 ANALYSIS OF THE CGPS ALGORITHM
lOOO 60 97 10001
Small problems Max
Avg 59
402500 lOO lOO
10355 10289
10139 10282 10238
Large problems
Min Max
42 82 10011 10300
10001 10243 10050
Overall problems Max 69
503478 100
922609 10355 10156
10001 10282 10115
The second experiment refers to the comparison of conditions (3) (7) and (9) in terms of the proportional equivalency (14) the relative error (15) and the proportional improvement (16) Table 2 gives the reshysults Notably the number of times of the cases C3 C4 C5 C6 C7 and C8 for conditions (7) and (9) are all zeros In other words the CGPS algorithm with condition (7) or (9) only generates the initial permutashytion schedule It follows that the proportional equivalency is lOO the relative error is zero and the proportional improvement is not availshyable (NA) for each problem These results indicate that the initial pershymutation schedule never violates the worst-case performance ratio of 53 if we adopt the CGPS algorithm with condition (7) or (9)
In the case of condition (3) viz the original CGPS algorithm the proportional equivalency is 2695 on the average This value indicates that 7305 of the desired performance (4) holds also when condition (3) is violated Therefore condition (3) is too restrictive especially for the large problems In concern with the improvement on the makesshypan of 1tl the relative error is 03940 and the proportional improveshyment is 647152 on the average This implies that 647154 of the permutation schedule 1t2 has improved on the initial permutation schedule in its makespan However the quality of improvement made by 1t2 over 1tl is only 03940 in terms of the empirical average-case performance ratio Therefore condition (3) in the CGPS algorithm is more restrictive and the improvement in the average-case performance ratio is so marginal from the empirical point of view
The third experiment concerns the comparison of the CGPS algoshyrithm with the NEH algorithm as well as the CDS algorithm Comparashytive results are shown in Table 3 On basis of the empirical
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ltgtl
ltgtl
Tab
le 2
C
om
para
tiv
e r
esu
lts
for
co
nd
itio
ns
(3)
(7)
an
d (
9)
--------------shy
Job
C
ondi
tion
s E
i R
i Ii
C
1 C
2 C
3 C
4 C
5 C
6 C
7 C
8 -----shy
(3)
59
39
0 0
2 0
0 0
951
220
0
0000
000
00
3 (7
) 59
41
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 59
41
0
0 0
0 0
0 10
000
00
0
0000
NA
-shy
(3)
53
39
4 0
0 0
4 0
829
787
0
3633
500
000
4 (7
) 53
47
0
0 0
0 0
0 10
000
00
0
0000
NA
53
47
0
0 0
0 0
0 10
000
00
0
0000
NA
---shy
-----------shy
(3)
40
44
9 0
1 0
6 0
733
333
0
4365
562
500
5 (7
) 40
60
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 40
eo
0
0 0
0 0
0 10
000
00
0
0000
NA
(3
) 40
4
7
5 0
0 0
8 0
783
333
0
3392
384
600
6 (7
) 40
6
0
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
40
60
0
0 0
0 0
0 10
000
00
0
000
0
NA
-----------shy
(3)
33
37
10
0
0 0
20
0 55
223
9
054
27
33
330
0
7 (7
) 33
6
7
0 0
0 0
0 0
100
0000
000
00
N
A
p (9
) 33
6
7
0 0
0 0
0 0
100
0000
000
00
N
A
0 ------~
(Tab
le 2
Con
td)
gtshy Z
C
)
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Con
diti
ons
~ z
Job
E
i R
i Ii
gt
C
l C
2 C
3 C
4 C
5 C
6 C
7 C
8 S CD
26
40
16
0 1
0 17
0
540
541
0
8209
470
600
CD
0 gt1j
8 (7
) 26
74
0
0 0
0 0
0 10
000
00
0
0000
NA
gt-3 r
(9)
26
74
0 0
0 0
0 0
100
0000
000
00
N
A
ttl
()
36
29
12
0 1
0 22
0
453
125
0
7050
342
900
0 0
9
(7)
36
64
0 0
0 0
0 0
100
0000
000
00
N
A
CD
t
(9)
36
64
0 0
0 0
0 0
100
0000
000
00
N
A
0 ~
0 0(3
) 35
29
19
0
0 0
17
0 44
615
4
079
33
52
780
0
gt-3 r10
(7
) 35
65
0
0 0
0 0
0 10
000
00
0
0000
NA
s
(9
) 35
65
0
0 0
0 0
0 10
000
00
0
0000
NA
(3
) 44
12
20
0
0 0
24
0 21
428
6
054
37
45
450
0
15
(7)
44
56
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
44
56
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
42
9 27
0
0 0
22
0 15
517
2
113
59
55
100
0
20
(7)
42
58
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
42
58
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
53
6 19
0
0 0
22
0 12
766
0
050
21
46
340
0
30
(7)
53
47
0 0
0 0
0 0
100
0000
000
00
N
A
53
47
0 ()
0 0
0 0
100
0000
000
00
N
A
CJ
) C
J)
Of
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w
Job
--~~
~~-
~--
Ri
Ii
w
CIl
Cl
C2
C
3 C
4
C5
C6
C7
C
8 -----------shy
(3)
51
3 21
0
1 0
24
0
61
22
4
0
46
39
456
500
40
(7
) 51
4
9
0 0
0 0
0 0
10
00
00
0
0
00
00
NA
(9
) 51
4
9
0 -----_
_
-shy
0 0
0 --shy-----~~~~
0 0
-----------shy
10
00
00
0
0
00
00
---------shy
NA
(3
) 4
8
4 33
0
0 0
15
0
769
23
0
62
20
68
75
00
50
(7
) 4
8
52
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
48
5
2
0 0
0 0
0 0
100
0000
00
00
0
N
A
51
4 3
7
0 0
0 8
0 8
16
33
05
05
8
8
22
20
0
10
0
(7)
51
49
0
0 0
0 0
0 10
000
00
0
00
00
NA
(9
) 51
4
9
0 0
0 0
0 0
100
0000
00
00
0
N
A
----------shy
(3)
56
0
35
0 0
0 9
0 0
00
00
02
29
1
79
550
0
20
0
(7)
56
4
4
0 0
0 0
0 0
100
0000
00
00
0
N
A
(9)
56
4
4
0 0
0 0
0 0
100
0000
00
00
0
N
A
69
2
24
0
0 0
5 0
64
51
6
0
13
97
82
76
00
30
0
(7)
69
31
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
(9)
69
31
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
(3)
62
1
35
0
0 0
2 0
26
31
6
0
17
96
94
59
00
~
40
0
(7)
62
3
8
0 0
0 0
0 0
10
00
00
0
0
00
00
NA
0
(9)
62
3
8
0 0
0 0
0 0
100
0000
00
00
0
N
A
fja
tJte
2-C
onat
)
gt-3
)shy z Cl
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0gtshy
Con
diti
ons
Job
E
i R
i Ii
C
l C
2 C
3 C
4 C
5 C
6 C
7 C
8 ~ rt
J gt-
lt57
0
38
0 0
0 5
0 0
0000
015
97
88
370
0
rtJ
j
500
(7)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
0 3
(9)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
tr1
tlj
0(3
) 57
1
39
0 0
0 3
0 2
3256
014
97
92
860
0
0 rtJ
60
0
(7)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
~ 57
43
0
0 0
0 0
0 10
000
00
0
0000
NA
0 0
1 34
0
gt-lt
(3)
64
0 0
1 0
277
78
0
1198
971
400
tO
3
700
(7)
64
36
0 0
0 0
0 0
100
0000
000
00
N
A
tr1 ~
(9)
64
36
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
60
2 38
0
0 0
0 0
500
00
0
0999
100
0000
800
(7)
60
40
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
60
40
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
62
0 38
0
0 0
0 0
000
00
0
0999
100
0000
900
(7)
62
38
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
62
38
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
60
0 39
0
0 0
1 0
000
0
010
99
97
500
0
1000
(7
) 60
40
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 60
40
0
0 0
0 0
0 10
000
00
0
0000
NA
w
w
(T
able
2 C
ontd
)
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------
CIJ
Con
diti
ons
CIJ
Job
E
i R
i Ii
O
J
C1
C2
C3
C4
C
5 C
6 C
7
C8
Min
44
615
4
000
00
0
0000
Sm
all
(3
) M
ax
951
220
0
8209
562
500
pro
ble
ms
Avg
66
121
6
050
01
39
021
3
_
Min
0
0000
009
99
45
450
0
Lar
ge
(3
) M
ax
214
286
1
1359
100
0000
p
rob
lem
s A
vg
605
84
0
3374
784
187
--
-_
__
---
Min
0
0000
000
00
0
0000
Ov
eral
l
(3)
Max
95
122
0
113
59
10
000
00
pro
ble
ms
Avg
26
950
0
039
40
64
715
2
tr1
()
gt-3 ~ Q
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gtshyT
able
3
z C
om
par
ativ
e re
sult
s fo
r th
e C
GP
S
CD
S
an
d N
EH
alg
ori
thm
s ~
Job 3
4
5
6
7
8
9
10
15
20
30
40
50
100
200
101
39
101
48
102
64
102
82
102
62
102
7
102
82
102
55
102
43
101
83
101
06
100
85
100
65
100
32
100
17
~CDS
100
96
101
28
102
14
102
32
102
46
102
15
102
04
102
04
101
78
101
53
100
99
100
63
100
55
100
35
100
17
~~--------------
~NElI
100
1
100
39
100
53
100
54
100
48
100
72
100
50
100
47
100
25
100
33
100
08
100
05
100
06
100
02
100
01
(Tab
le 3
Con
td)
lfl
gt-
lfl
0 tJ r t=J
0 0 i
lfl gtshy r 0 0 ~ r $
w
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to
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-----
---
co
jgt
~CGPS
~CDS
~NE
H 0
300
100
07
100
11
100
00
400
100
04
100
06
100
00
500
100
02
100
06
100
00
600
100
02
100
03
100
00
700
100
01
100
02
100
00
800
100
02
100
02
100
00
900
100
01
100
04
100
00
1000
1
0001
1
0003
1
0000
Min
1
0139
1
0096
1
0010
Sm
all
pro
ble
ms
Max
1
0282
1
0246
1
0072
Avg
1
0238
1
0192
1
0047
Min
1
0001
1
0002
1
0000
Lar
ge
pro
ble
ms
Max
1
0243
1
0178
1
0033
Avg
1
0050
1
0042
1
0005
Min
1
0001
1
0002
1
0000
r
Ov
eral
l p
rob
lem
s M
ax
102
82
102
46
100
72
0 -3A
vg
101
15
100
95
100
20
p Z
(Tab
le 3
Con
td)
0
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lgtT
able
3 (
Con
td)
Z
--------
_shy
SCG
PS
EQ
CG
PS
SCD
S SC
GP
S E
QC
GP
S SN
EH
SN
EH
EQ
IjE
H
SCD
S --
~ Jo
b C
DS
CDS
CG
PS
NEH
N
EH
CG
PS
CD
S CD
S N
EH
en
en
3 0
91
9
0
83
17
12
88
0
0 gtzj
4 2
8
7
11
2
70
28
21
76
3
gt-l
[1j
5 2
82
16
4
58
38
28
66
6
0 0 1
j6
3
77
20
1
51
48
40
56
4
en
7 5
75
20
5
43
52
45
50
5
~ 0 0
8 6
70
24
8
35
57
44
47
9
~
gt-l
9 5
6
7
28
2
40
58
46
46
8
10
7
68
25
5
40
55
42
50
8
15
7
70
23
2
45
53
46
50
4
20
8
65
27
4
46
50
37
58
5
30
8
74
18
0
57
43
35
63
2
40
7
72
21
3
53
44
29
62
9
50
13
62
25
4
49
47
31
59
10
10
0
15
6
0
25
1
52
47
27
71
2
20
0
17
64
19
2
55
43
26
68
6
30
0
12
74
14
7
63
30
17
74
9
w
(T
able
3 C
ontd
)
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Job
S
co
Ps
CD
S E
QC
GP
SC
DS
SCD
S C
GPS
SC
GP
S N
EH
E
QC
GP
SN
EH
S
NE
H
CG
PS
NE
H
SCD
S E
Q
EH
CD
S SC
DS
NE
Il
N
400
14
63
23
5
58
37
14
81
5
500
13
62
25
1
57
42
20
78
2
60
0
9
61
30
4
54
42
12
82
6
700
11
66
23
8
56
36
13
79
8
80
0
10
63
27
4
57
39
14
82
4
900
16
63
21
2
60
38
17
81
2
1000
11
61
28
4
57
39
12
82
6
Sm
all
pro
ble
ms
Min
Max
Avg
0
67
9
7
91
28
375
00
77
125
0
191
250
0
8
337
50
35
17
83
58
525
000
44
1250
12
46
347
500
46
88
598
750
0
9
537
50
Lar
ge
pro
ble
ms
Min
Max
7
17
60
74
14
30
0
8
45
63
30
53
12
46
50
82
2
10
114
00
65
3333
2
326
67
3
4000
546
000
42
0000
233
333
71
333
3
533
33
Ov
eral
l p
rob
lem
s
Min
Max
Avg
0
60
9
17
91
30
873
91
69
434
8 2
182
61
0
8
339
13
35
17
83
58
538
696
42
739
1
12
46
273
043
~~~--~--
46
0
88
10
673
478
5
3478
c 0 ~ gtshy L
0
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343 ANALYSIS OF THE CGPS ALGORITHM
average-case performance ratio the NEH algorithm is the best one folshylowed by the CDS algorithm and then by the CGPS algorithm As comshypared with the CDS algorithm in terms of the proportional superiority 87391 of problem instances better by the CGPS algorithm 694348 of problem instances equal by the CGPS and CDS algorithms and 218261 of problem instances better by the CDS algorithm on the average It follows that the CDS algorithm is better than the CGPS alshygorithm especially for the large problems Similarly the NEH algorithm is better than the CGPS algorithm and the NEH algorithm is better than the CDS algorithm especially for the small problems Therefore we have the same conclusion about these heuristics in terms of the empirishycal average-case performance ratio and proportional superiority
To sum up 503478 ofthe initial permutation schedules generated by the CGPS algorithm are the optimal schedules Among the 496522 of non-optimal schedules 647154 of the permutation schedules 1t2 improve the makespan of the initial permutation schedule However the improvement made by 1t2 is only 03940 over 1tl in terms of the empirical average-case performance ratio Moreover compared with the NEH and CDS algorithms the CGPS algorithm is the worst one in terms of the empirical average-case performance ratio and proportional superiority These imply that the CGPS algorithm is not good enough for applications
5 CONCLUSION
In this paper the 3FSS with minimum makespan criterion is considered Theoretically the heuristic algorithm proposed by Chen et a1 [2] has the best worst-case performance ratio of 53 This paper evaluates the performance of the CGPS algorithm from the empirical perspective Three simulation experiments are performed to analyze its performance in terms of the six evaluation measures on a total of 13800 problems Four conclusions can be drawn from this paper Firstly in the CGPS algorithm 503478 of the initial permutation schedules are the optimal ones 321326 of the permutation schedshyules 1t2 improve the makespan of nl and 175196 of the permutation schedules n2 do not improve their makespan Secondly the empirical average-case performance ratio of initial permutation schedule genershyated by the CGPS algorithm is always smaller than the performance guarantee 53 developed by Chen et al [2] Its empirical average-case performance ratio is in the range of 10011 and 10355 and is 10156 on the average Thirdly the permutation schedule 1t2 improves 1tl in terms of the empirical average-case performance ratio by the non-significant percentage 03940 Fourthly among the three heuristic algorithms the
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344 H C TANG
NEH algorithm is the best one in terms of both empirical average-case performance ratio and proportional superiority followed by the CDS alshygorithm and then by the CGPS algorithm Consequently the CGPS algoshyrithm is not suitable for the 3FSS with minimum makespan criterion
REFERENCES
l H G Campbell R A Dudek and M L Smith A heuristic algorithm for the n-job tn-machine sequencing problem Management Science Vol 16 (1970) pp 630-637
2 B Chen C A Glass C N Potts and V A Strusevich A new heuristic for threeshymachine flow shop scheduling Operations Research Vol 44 (1996) pp 891-898
3 R W Conway W L Maxwell and L W Miller Theory of Scheduling AddisonshyWesley Reading MA 1967
4 R A Dudek S S Panwalkar and M L Smith The lessons of flowshop scheduling research Operations Research Vol 40 (1992) pp 7-13
5 M R Garey D S Johnson and R Sethi The complexity of flowshop and jobshop scheduling Mathematics Operations Research Vol 1 (1976) pp 117-129
6 S M Johnson Optimal two- and three-stage production schedules with setup times included Naval Research Logistic Quarterly Vol 1 (1954) pp 61-68
7 B J Lageweg J K Lenstra and A H G Rinnooy Kan A general bounding scheme for the permutation flow-shop problem Operations Research Vol 26 (1978) pp 53-67
8 E L Lawler J K Lenstra A H G Rinnooy Kan and D B Shmoys Sequencing and Scheduling Algorithms and Complexity in S C Graves A H G Rinnooy
-Kan and P H Zipkin Handbooks in Operations Research and Management Science Vol 4 Logistics of Production and Inventory North Holland Amsterdam 1993 pp 445-522
9 J K Lenstra A H G Rinnooy Kan and P Brucker Complexity of machine scheduling problems Annals Discrete Mathematics Vol 1 (1977) pp 343-362
10 M Nawaz E E Enscore Jr and I Ham A heuristic algorithm for the m-machine n-job flow-shop sequencing problem OMEGA Vol 11 (1983) pp 91-95
II C N Potts An Adaptive branching rule for the permutation flow-shop problem European Journal of Operational Research Vol 5 (1980) pp 19-25
12 A H G Rinnooy Kan Machine scheduling problems classification complexity and computations Nijhoff The Hague 1976
13 H Rock and G Schmidt Machine aggregation heuristics in shop scheduling Methods of Operations Research Vol 45 (1983) pp 303-314
14 C Smutnicki Some results of the worst-case analysis for flow shop scheduling European Journal of Operational Research Vol 109 (1998) pp 66-87
15 S M A Suliman A two-phase heuristic approach to the permutation flow-shop scheduling problem International Journal of Production Economics Vol 64 (2000) pp 143-152
16 H C Tang A new lower bonding rule for permutation flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 249-257
17 H C Tang A modification to the CGPS algorithm for three-machine flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 321-331
Received January 2001
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330 HCTANG
guished One is the relative error of the schedule 1tl from the schedule 1tH in terms of the 11Z)
i [ Cmax(1tH Z)Cmax(1t Z) 1R 1- 100 Cmax(1tl Z)CmaxC1t Z)
Cmax(1tH Z) ] (15) 1 - C ( l Z) 100 for pound 3 7 9[ 1tmax
The other is an improvement on the makespan of the schedule 1t1 in terms of the problem instance The definition of this proportional imshyprovement is
Ii _ [ (C3) + (C4) ] - (C3) +(C4) +(C5) +(C6) +(C7) +(C8) 100
for i 3 7 9 (16)
where (C3) + (C4) is the number of times the schedule 1tH that imshyproves the makespan of the schedule 1tl
Finally in concern with the comparison of the CGPS NEH and CDS algorithms two evaluation measures are adopted One measure is based on the empirical average-case performance ratio (13) The other measure is that the percentage of problem instances that the A algorithm is superior to the B algorithm in its makespan This proporshytional superiority can be defined as
A _[ (A) ] (17)SB - (A) +(AB) + (B) 100
where (A) and (B) are the number of times the makespan better by A and B algorithms respectively and (AB) is the number of times the makespan equal by A and B algorithms Denote E~ the percentage of problem instances that the A algorithm is equal to the B algorithm
Therefore there are six evaluation measures adopted in this paper We use (11) and (13) to measure the effectiveness of the CGPS algoshyrithm To compare conditions (3) (7) and (9) satisfying the desired pershyformance (4) three evaluation measures (14) (15) and (16) are adopted To compare the CGPS algorithm with the NEH algorithm as well as the CDS algorithm two evaluation measures (13) and (17) are used
4 SIMULATION EXPERIMENTS
To get an idea of the CGPS algorithm from the empirical perspecshytive three simulation experiments are conducted in this paper The first simulation experiment examines the effectiveness of the CGPS algoshy
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331 ANALYSIS OF THE CGPS ALGORITHM
rithm The second simulation experiment concerns the empirical comparishyson of conditions (3) (7) and (9) which are the sufficient for the desired performance (4) The third simulation experiment conducts the comparison of the CGPS algorithm with the NEH and CDS algorithms
The number of machines considered is three and the numbers of jobs considered are 3 4 5 6 7 8 9 10 15 20 30 40 50 100 200 300 400 500 600 700 800 900 and 1000 The problem size is dishyvided into two classes One is the small problem for the 3 4 5 6 7 8 9 and 10 job problems The other is the large problem for the 15 20 30 40 50 100 200 300 400 500 600 700 800 900 and 1000 job problems One hundred problems are generated for each job The processing times for each problem are randomly generated integers uniformly distributed between 1 and 100 A more portable random number generator Xn == 16807xH(mod 231
- 1) is adopted using same to generate the random values for each problem The whole simushy
lation experiments are subjected to extensive testing 13800 problems A number of evaluation measures described in previous section
are used for the three simulation experiments We adopt the evaluation measures (11) and (13) for the first simulation experiment (14) (15) and (16) for the second simulation experiment (13) and (17) for the third simulation experiment respectively The whole computation is conducted on a 266 MHz Pentinum II PC using Microsoft Visual C++ compiler under Microsoft Windows 98 operating system
To obtain an optimal schedule for the empirical average-case pershyformance ratio the branch-and-bound algorithm with two-machine lower bound rule proposed by Tang [16] is applied This proposed twoshymachine lower bound is a modification to the algorithms of Lageweg et al [7] and Potts [11] and is shown to be more efficient Whenever a problem is not solved after 100000 nodes have been generated comshyputation is stopped In this case the optimal makespan is replaced by the best-known makespan Thus the empirical average-case pershyformance ratio is underestimated
The first simulation experiment is concerned with the effectiveness of the CGPS algorithm Detailed results are presented in Table 1 where Min denotes the minimum value of proportional optimum or empirical average-case performance ratio Max and Avg the corresshyponding maximum and average values respectively The proportional optimum is 503478 on the average In other words 503478 of the initial permutation schedules generated by the CGPS algorithm are the optimal schedules especially for the large problems the empirical average-case performance ratio is concerned Table 1 lists the percentage POP of problem instances that the optimal schedules are found by the
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332 H C TANG
branch-and-bound algorithm with Tangs two-machine lower bound rule [16] The average percentage Popt is 962609 Especially in the case of small problems the branch-and-bound algorithm can always find the optimal schedules within 100000 nodes generated Therefore it is appropriate to adopt the empirical average-ca~e performance ratio to measure the effectiveness of the CGPS algorithm The empirical average-case performance ratio of the initial permutation schedule is in the range of 10011 and 10355 and is 10156 on the average While the empirical average-case performance ratio of the CGPS algorithm is in the range of 10001 and 10282 and is 10115 on the average These values are always smaller than the performance guarantee 53 of the CGPS algorithm
Table 1 Results of simulations for the effectiveness of the COPS
Job P Popt ~1t1
3 59 100 10139 4 53 100 10185 10148 5 40 100 10309 10264 6 40 100 10317 10282 7 33 100 10318 10262 8 26 100 10355 10270 9 36 100 10355 10282 10 35 100 10337 10255 15 44 84 10299 10243 20 42 82 10300 10183 30 53 90 10157 10106 40 51 94 10132 10085 50 48 95 10128 10065 100 51 93 10083 10032 200 56 97 10040 10017 300 69 99 10021 10007 400 62 98 10022 10004 500 57 96 10018 10002 600 57 99 10017 10002 700 64 96 10013 10001 800 60 99 10012 10002
(Table 1 Contd)
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333 ANALYSIS OF THE CGPS ALGORITHM
lOOO 60 97 10001
Small problems Max
Avg 59
402500 lOO lOO
10355 10289
10139 10282 10238
Large problems
Min Max
42 82 10011 10300
10001 10243 10050
Overall problems Max 69
503478 100
922609 10355 10156
10001 10282 10115
The second experiment refers to the comparison of conditions (3) (7) and (9) in terms of the proportional equivalency (14) the relative error (15) and the proportional improvement (16) Table 2 gives the reshysults Notably the number of times of the cases C3 C4 C5 C6 C7 and C8 for conditions (7) and (9) are all zeros In other words the CGPS algorithm with condition (7) or (9) only generates the initial permutashytion schedule It follows that the proportional equivalency is lOO the relative error is zero and the proportional improvement is not availshyable (NA) for each problem These results indicate that the initial pershymutation schedule never violates the worst-case performance ratio of 53 if we adopt the CGPS algorithm with condition (7) or (9)
In the case of condition (3) viz the original CGPS algorithm the proportional equivalency is 2695 on the average This value indicates that 7305 of the desired performance (4) holds also when condition (3) is violated Therefore condition (3) is too restrictive especially for the large problems In concern with the improvement on the makesshypan of 1tl the relative error is 03940 and the proportional improveshyment is 647152 on the average This implies that 647154 of the permutation schedule 1t2 has improved on the initial permutation schedule in its makespan However the quality of improvement made by 1t2 over 1tl is only 03940 in terms of the empirical average-case performance ratio Therefore condition (3) in the CGPS algorithm is more restrictive and the improvement in the average-case performance ratio is so marginal from the empirical point of view
The third experiment concerns the comparison of the CGPS algoshyrithm with the NEH algorithm as well as the CDS algorithm Comparashytive results are shown in Table 3 On basis of the empirical
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ltgtl
ltgtl
Tab
le 2
C
om
para
tiv
e r
esu
lts
for
co
nd
itio
ns
(3)
(7)
an
d (
9)
--------------shy
Job
C
ondi
tion
s E
i R
i Ii
C
1 C
2 C
3 C
4 C
5 C
6 C
7 C
8 -----shy
(3)
59
39
0 0
2 0
0 0
951
220
0
0000
000
00
3 (7
) 59
41
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 59
41
0
0 0
0 0
0 10
000
00
0
0000
NA
-shy
(3)
53
39
4 0
0 0
4 0
829
787
0
3633
500
000
4 (7
) 53
47
0
0 0
0 0
0 10
000
00
0
0000
NA
53
47
0
0 0
0 0
0 10
000
00
0
0000
NA
---shy
-----------shy
(3)
40
44
9 0
1 0
6 0
733
333
0
4365
562
500
5 (7
) 40
60
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 40
eo
0
0 0
0 0
0 10
000
00
0
0000
NA
(3
) 40
4
7
5 0
0 0
8 0
783
333
0
3392
384
600
6 (7
) 40
6
0
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
40
60
0
0 0
0 0
0 10
000
00
0
000
0
NA
-----------shy
(3)
33
37
10
0
0 0
20
0 55
223
9
054
27
33
330
0
7 (7
) 33
6
7
0 0
0 0
0 0
100
0000
000
00
N
A
p (9
) 33
6
7
0 0
0 0
0 0
100
0000
000
00
N
A
0 ------~
(Tab
le 2
Con
td)
gtshy Z
C
)
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Con
diti
ons
~ z
Job
E
i R
i Ii
gt
C
l C
2 C
3 C
4 C
5 C
6 C
7 C
8 S CD
26
40
16
0 1
0 17
0
540
541
0
8209
470
600
CD
0 gt1j
8 (7
) 26
74
0
0 0
0 0
0 10
000
00
0
0000
NA
gt-3 r
(9)
26
74
0 0
0 0
0 0
100
0000
000
00
N
A
ttl
()
36
29
12
0 1
0 22
0
453
125
0
7050
342
900
0 0
9
(7)
36
64
0 0
0 0
0 0
100
0000
000
00
N
A
CD
t
(9)
36
64
0 0
0 0
0 0
100
0000
000
00
N
A
0 ~
0 0(3
) 35
29
19
0
0 0
17
0 44
615
4
079
33
52
780
0
gt-3 r10
(7
) 35
65
0
0 0
0 0
0 10
000
00
0
0000
NA
s
(9
) 35
65
0
0 0
0 0
0 10
000
00
0
0000
NA
(3
) 44
12
20
0
0 0
24
0 21
428
6
054
37
45
450
0
15
(7)
44
56
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
44
56
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
42
9 27
0
0 0
22
0 15
517
2
113
59
55
100
0
20
(7)
42
58
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
42
58
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
53
6 19
0
0 0
22
0 12
766
0
050
21
46
340
0
30
(7)
53
47
0 0
0 0
0 0
100
0000
000
00
N
A
53
47
0 ()
0 0
0 0
100
0000
000
00
N
A
CJ
) C
J)
Of
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w
Job
--~~
~~-
~--
Ri
Ii
w
CIl
Cl
C2
C
3 C
4
C5
C6
C7
C
8 -----------shy
(3)
51
3 21
0
1 0
24
0
61
22
4
0
46
39
456
500
40
(7
) 51
4
9
0 0
0 0
0 0
10
00
00
0
0
00
00
NA
(9
) 51
4
9
0 -----_
_
-shy
0 0
0 --shy-----~~~~
0 0
-----------shy
10
00
00
0
0
00
00
---------shy
NA
(3
) 4
8
4 33
0
0 0
15
0
769
23
0
62
20
68
75
00
50
(7
) 4
8
52
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
48
5
2
0 0
0 0
0 0
100
0000
00
00
0
N
A
51
4 3
7
0 0
0 8
0 8
16
33
05
05
8
8
22
20
0
10
0
(7)
51
49
0
0 0
0 0
0 10
000
00
0
00
00
NA
(9
) 51
4
9
0 0
0 0
0 0
100
0000
00
00
0
N
A
----------shy
(3)
56
0
35
0 0
0 9
0 0
00
00
02
29
1
79
550
0
20
0
(7)
56
4
4
0 0
0 0
0 0
100
0000
00
00
0
N
A
(9)
56
4
4
0 0
0 0
0 0
100
0000
00
00
0
N
A
69
2
24
0
0 0
5 0
64
51
6
0
13
97
82
76
00
30
0
(7)
69
31
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
(9)
69
31
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
(3)
62
1
35
0
0 0
2 0
26
31
6
0
17
96
94
59
00
~
40
0
(7)
62
3
8
0 0
0 0
0 0
10
00
00
0
0
00
00
NA
0
(9)
62
3
8
0 0
0 0
0 0
100
0000
00
00
0
N
A
fja
tJte
2-C
onat
)
gt-3
)shy z Cl
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0gtshy
Con
diti
ons
Job
E
i R
i Ii
C
l C
2 C
3 C
4 C
5 C
6 C
7 C
8 ~ rt
J gt-
lt57
0
38
0 0
0 5
0 0
0000
015
97
88
370
0
rtJ
j
500
(7)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
0 3
(9)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
tr1
tlj
0(3
) 57
1
39
0 0
0 3
0 2
3256
014
97
92
860
0
0 rtJ
60
0
(7)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
~ 57
43
0
0 0
0 0
0 10
000
00
0
0000
NA
0 0
1 34
0
gt-lt
(3)
64
0 0
1 0
277
78
0
1198
971
400
tO
3
700
(7)
64
36
0 0
0 0
0 0
100
0000
000
00
N
A
tr1 ~
(9)
64
36
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
60
2 38
0
0 0
0 0
500
00
0
0999
100
0000
800
(7)
60
40
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
60
40
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
62
0 38
0
0 0
0 0
000
00
0
0999
100
0000
900
(7)
62
38
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
62
38
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
60
0 39
0
0 0
1 0
000
0
010
99
97
500
0
1000
(7
) 60
40
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 60
40
0
0 0
0 0
0 10
000
00
0
0000
NA
w
w
(T
able
2 C
ontd
)
-l
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------
CIJ
Con
diti
ons
CIJ
Job
E
i R
i Ii
O
J
C1
C2
C3
C4
C
5 C
6 C
7
C8
Min
44
615
4
000
00
0
0000
Sm
all
(3
) M
ax
951
220
0
8209
562
500
pro
ble
ms
Avg
66
121
6
050
01
39
021
3
_
Min
0
0000
009
99
45
450
0
Lar
ge
(3
) M
ax
214
286
1
1359
100
0000
p
rob
lem
s A
vg
605
84
0
3374
784
187
--
-_
__
---
Min
0
0000
000
00
0
0000
Ov
eral
l
(3)
Max
95
122
0
113
59
10
000
00
pro
ble
ms
Avg
26
950
0
039
40
64
715
2
tr1
()
gt-3 ~ Q
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gtshyT
able
3
z C
om
par
ativ
e re
sult
s fo
r th
e C
GP
S
CD
S
an
d N
EH
alg
ori
thm
s ~
Job 3
4
5
6
7
8
9
10
15
20
30
40
50
100
200
101
39
101
48
102
64
102
82
102
62
102
7
102
82
102
55
102
43
101
83
101
06
100
85
100
65
100
32
100
17
~CDS
100
96
101
28
102
14
102
32
102
46
102
15
102
04
102
04
101
78
101
53
100
99
100
63
100
55
100
35
100
17
~~--------------
~NElI
100
1
100
39
100
53
100
54
100
48
100
72
100
50
100
47
100
25
100
33
100
08
100
05
100
06
100
02
100
01
(Tab
le 3
Con
td)
lfl
gt-
lfl
0 tJ r t=J
0 0 i
lfl gtshy r 0 0 ~ r $
w
w
to
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-----
---
co
jgt
~CGPS
~CDS
~NE
H 0
300
100
07
100
11
100
00
400
100
04
100
06
100
00
500
100
02
100
06
100
00
600
100
02
100
03
100
00
700
100
01
100
02
100
00
800
100
02
100
02
100
00
900
100
01
100
04
100
00
1000
1
0001
1
0003
1
0000
Min
1
0139
1
0096
1
0010
Sm
all
pro
ble
ms
Max
1
0282
1
0246
1
0072
Avg
1
0238
1
0192
1
0047
Min
1
0001
1
0002
1
0000
Lar
ge
pro
ble
ms
Max
1
0243
1
0178
1
0033
Avg
1
0050
1
0042
1
0005
Min
1
0001
1
0002
1
0000
r
Ov
eral
l p
rob
lem
s M
ax
102
82
102
46
100
72
0 -3A
vg
101
15
100
95
100
20
p Z
(Tab
le 3
Con
td)
0
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lgtT
able
3 (
Con
td)
Z
--------
_shy
SCG
PS
EQ
CG
PS
SCD
S SC
GP
S E
QC
GP
S SN
EH
SN
EH
EQ
IjE
H
SCD
S --
~ Jo
b C
DS
CDS
CG
PS
NEH
N
EH
CG
PS
CD
S CD
S N
EH
en
en
3 0
91
9
0
83
17
12
88
0
0 gtzj
4 2
8
7
11
2
70
28
21
76
3
gt-l
[1j
5 2
82
16
4
58
38
28
66
6
0 0 1
j6
3
77
20
1
51
48
40
56
4
en
7 5
75
20
5
43
52
45
50
5
~ 0 0
8 6
70
24
8
35
57
44
47
9
~
gt-l
9 5
6
7
28
2
40
58
46
46
8
10
7
68
25
5
40
55
42
50
8
15
7
70
23
2
45
53
46
50
4
20
8
65
27
4
46
50
37
58
5
30
8
74
18
0
57
43
35
63
2
40
7
72
21
3
53
44
29
62
9
50
13
62
25
4
49
47
31
59
10
10
0
15
6
0
25
1
52
47
27
71
2
20
0
17
64
19
2
55
43
26
68
6
30
0
12
74
14
7
63
30
17
74
9
w
(T
able
3 C
ontd
)
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Job
S
co
Ps
CD
S E
QC
GP
SC
DS
SCD
S C
GPS
SC
GP
S N
EH
E
QC
GP
SN
EH
S
NE
H
CG
PS
NE
H
SCD
S E
Q
EH
CD
S SC
DS
NE
Il
N
400
14
63
23
5
58
37
14
81
5
500
13
62
25
1
57
42
20
78
2
60
0
9
61
30
4
54
42
12
82
6
700
11
66
23
8
56
36
13
79
8
80
0
10
63
27
4
57
39
14
82
4
900
16
63
21
2
60
38
17
81
2
1000
11
61
28
4
57
39
12
82
6
Sm
all
pro
ble
ms
Min
Max
Avg
0
67
9
7
91
28
375
00
77
125
0
191
250
0
8
337
50
35
17
83
58
525
000
44
1250
12
46
347
500
46
88
598
750
0
9
537
50
Lar
ge
pro
ble
ms
Min
Max
7
17
60
74
14
30
0
8
45
63
30
53
12
46
50
82
2
10
114
00
65
3333
2
326
67
3
4000
546
000
42
0000
233
333
71
333
3
533
33
Ov
eral
l p
rob
lem
s
Min
Max
Avg
0
60
9
17
91
30
873
91
69
434
8 2
182
61
0
8
339
13
35
17
83
58
538
696
42
739
1
12
46
273
043
~~~--~--
46
0
88
10
673
478
5
3478
c 0 ~ gtshy L
0
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343 ANALYSIS OF THE CGPS ALGORITHM
average-case performance ratio the NEH algorithm is the best one folshylowed by the CDS algorithm and then by the CGPS algorithm As comshypared with the CDS algorithm in terms of the proportional superiority 87391 of problem instances better by the CGPS algorithm 694348 of problem instances equal by the CGPS and CDS algorithms and 218261 of problem instances better by the CDS algorithm on the average It follows that the CDS algorithm is better than the CGPS alshygorithm especially for the large problems Similarly the NEH algorithm is better than the CGPS algorithm and the NEH algorithm is better than the CDS algorithm especially for the small problems Therefore we have the same conclusion about these heuristics in terms of the empirishycal average-case performance ratio and proportional superiority
To sum up 503478 ofthe initial permutation schedules generated by the CGPS algorithm are the optimal schedules Among the 496522 of non-optimal schedules 647154 of the permutation schedules 1t2 improve the makespan of the initial permutation schedule However the improvement made by 1t2 is only 03940 over 1tl in terms of the empirical average-case performance ratio Moreover compared with the NEH and CDS algorithms the CGPS algorithm is the worst one in terms of the empirical average-case performance ratio and proportional superiority These imply that the CGPS algorithm is not good enough for applications
5 CONCLUSION
In this paper the 3FSS with minimum makespan criterion is considered Theoretically the heuristic algorithm proposed by Chen et a1 [2] has the best worst-case performance ratio of 53 This paper evaluates the performance of the CGPS algorithm from the empirical perspective Three simulation experiments are performed to analyze its performance in terms of the six evaluation measures on a total of 13800 problems Four conclusions can be drawn from this paper Firstly in the CGPS algorithm 503478 of the initial permutation schedules are the optimal ones 321326 of the permutation schedshyules 1t2 improve the makespan of nl and 175196 of the permutation schedules n2 do not improve their makespan Secondly the empirical average-case performance ratio of initial permutation schedule genershyated by the CGPS algorithm is always smaller than the performance guarantee 53 developed by Chen et al [2] Its empirical average-case performance ratio is in the range of 10011 and 10355 and is 10156 on the average Thirdly the permutation schedule 1t2 improves 1tl in terms of the empirical average-case performance ratio by the non-significant percentage 03940 Fourthly among the three heuristic algorithms the
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344 H C TANG
NEH algorithm is the best one in terms of both empirical average-case performance ratio and proportional superiority followed by the CDS alshygorithm and then by the CGPS algorithm Consequently the CGPS algoshyrithm is not suitable for the 3FSS with minimum makespan criterion
REFERENCES
l H G Campbell R A Dudek and M L Smith A heuristic algorithm for the n-job tn-machine sequencing problem Management Science Vol 16 (1970) pp 630-637
2 B Chen C A Glass C N Potts and V A Strusevich A new heuristic for threeshymachine flow shop scheduling Operations Research Vol 44 (1996) pp 891-898
3 R W Conway W L Maxwell and L W Miller Theory of Scheduling AddisonshyWesley Reading MA 1967
4 R A Dudek S S Panwalkar and M L Smith The lessons of flowshop scheduling research Operations Research Vol 40 (1992) pp 7-13
5 M R Garey D S Johnson and R Sethi The complexity of flowshop and jobshop scheduling Mathematics Operations Research Vol 1 (1976) pp 117-129
6 S M Johnson Optimal two- and three-stage production schedules with setup times included Naval Research Logistic Quarterly Vol 1 (1954) pp 61-68
7 B J Lageweg J K Lenstra and A H G Rinnooy Kan A general bounding scheme for the permutation flow-shop problem Operations Research Vol 26 (1978) pp 53-67
8 E L Lawler J K Lenstra A H G Rinnooy Kan and D B Shmoys Sequencing and Scheduling Algorithms and Complexity in S C Graves A H G Rinnooy
-Kan and P H Zipkin Handbooks in Operations Research and Management Science Vol 4 Logistics of Production and Inventory North Holland Amsterdam 1993 pp 445-522
9 J K Lenstra A H G Rinnooy Kan and P Brucker Complexity of machine scheduling problems Annals Discrete Mathematics Vol 1 (1977) pp 343-362
10 M Nawaz E E Enscore Jr and I Ham A heuristic algorithm for the m-machine n-job flow-shop sequencing problem OMEGA Vol 11 (1983) pp 91-95
II C N Potts An Adaptive branching rule for the permutation flow-shop problem European Journal of Operational Research Vol 5 (1980) pp 19-25
12 A H G Rinnooy Kan Machine scheduling problems classification complexity and computations Nijhoff The Hague 1976
13 H Rock and G Schmidt Machine aggregation heuristics in shop scheduling Methods of Operations Research Vol 45 (1983) pp 303-314
14 C Smutnicki Some results of the worst-case analysis for flow shop scheduling European Journal of Operational Research Vol 109 (1998) pp 66-87
15 S M A Suliman A two-phase heuristic approach to the permutation flow-shop scheduling problem International Journal of Production Economics Vol 64 (2000) pp 143-152
16 H C Tang A new lower bonding rule for permutation flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 249-257
17 H C Tang A modification to the CGPS algorithm for three-machine flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 321-331
Received January 2001
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331 ANALYSIS OF THE CGPS ALGORITHM
rithm The second simulation experiment concerns the empirical comparishyson of conditions (3) (7) and (9) which are the sufficient for the desired performance (4) The third simulation experiment conducts the comparison of the CGPS algorithm with the NEH and CDS algorithms
The number of machines considered is three and the numbers of jobs considered are 3 4 5 6 7 8 9 10 15 20 30 40 50 100 200 300 400 500 600 700 800 900 and 1000 The problem size is dishyvided into two classes One is the small problem for the 3 4 5 6 7 8 9 and 10 job problems The other is the large problem for the 15 20 30 40 50 100 200 300 400 500 600 700 800 900 and 1000 job problems One hundred problems are generated for each job The processing times for each problem are randomly generated integers uniformly distributed between 1 and 100 A more portable random number generator Xn == 16807xH(mod 231
- 1) is adopted using same to generate the random values for each problem The whole simushy
lation experiments are subjected to extensive testing 13800 problems A number of evaluation measures described in previous section
are used for the three simulation experiments We adopt the evaluation measures (11) and (13) for the first simulation experiment (14) (15) and (16) for the second simulation experiment (13) and (17) for the third simulation experiment respectively The whole computation is conducted on a 266 MHz Pentinum II PC using Microsoft Visual C++ compiler under Microsoft Windows 98 operating system
To obtain an optimal schedule for the empirical average-case pershyformance ratio the branch-and-bound algorithm with two-machine lower bound rule proposed by Tang [16] is applied This proposed twoshymachine lower bound is a modification to the algorithms of Lageweg et al [7] and Potts [11] and is shown to be more efficient Whenever a problem is not solved after 100000 nodes have been generated comshyputation is stopped In this case the optimal makespan is replaced by the best-known makespan Thus the empirical average-case pershyformance ratio is underestimated
The first simulation experiment is concerned with the effectiveness of the CGPS algorithm Detailed results are presented in Table 1 where Min denotes the minimum value of proportional optimum or empirical average-case performance ratio Max and Avg the corresshyponding maximum and average values respectively The proportional optimum is 503478 on the average In other words 503478 of the initial permutation schedules generated by the CGPS algorithm are the optimal schedules especially for the large problems the empirical average-case performance ratio is concerned Table 1 lists the percentage POP of problem instances that the optimal schedules are found by the
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332 H C TANG
branch-and-bound algorithm with Tangs two-machine lower bound rule [16] The average percentage Popt is 962609 Especially in the case of small problems the branch-and-bound algorithm can always find the optimal schedules within 100000 nodes generated Therefore it is appropriate to adopt the empirical average-ca~e performance ratio to measure the effectiveness of the CGPS algorithm The empirical average-case performance ratio of the initial permutation schedule is in the range of 10011 and 10355 and is 10156 on the average While the empirical average-case performance ratio of the CGPS algorithm is in the range of 10001 and 10282 and is 10115 on the average These values are always smaller than the performance guarantee 53 of the CGPS algorithm
Table 1 Results of simulations for the effectiveness of the COPS
Job P Popt ~1t1
3 59 100 10139 4 53 100 10185 10148 5 40 100 10309 10264 6 40 100 10317 10282 7 33 100 10318 10262 8 26 100 10355 10270 9 36 100 10355 10282 10 35 100 10337 10255 15 44 84 10299 10243 20 42 82 10300 10183 30 53 90 10157 10106 40 51 94 10132 10085 50 48 95 10128 10065 100 51 93 10083 10032 200 56 97 10040 10017 300 69 99 10021 10007 400 62 98 10022 10004 500 57 96 10018 10002 600 57 99 10017 10002 700 64 96 10013 10001 800 60 99 10012 10002
(Table 1 Contd)
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333 ANALYSIS OF THE CGPS ALGORITHM
lOOO 60 97 10001
Small problems Max
Avg 59
402500 lOO lOO
10355 10289
10139 10282 10238
Large problems
Min Max
42 82 10011 10300
10001 10243 10050
Overall problems Max 69
503478 100
922609 10355 10156
10001 10282 10115
The second experiment refers to the comparison of conditions (3) (7) and (9) in terms of the proportional equivalency (14) the relative error (15) and the proportional improvement (16) Table 2 gives the reshysults Notably the number of times of the cases C3 C4 C5 C6 C7 and C8 for conditions (7) and (9) are all zeros In other words the CGPS algorithm with condition (7) or (9) only generates the initial permutashytion schedule It follows that the proportional equivalency is lOO the relative error is zero and the proportional improvement is not availshyable (NA) for each problem These results indicate that the initial pershymutation schedule never violates the worst-case performance ratio of 53 if we adopt the CGPS algorithm with condition (7) or (9)
In the case of condition (3) viz the original CGPS algorithm the proportional equivalency is 2695 on the average This value indicates that 7305 of the desired performance (4) holds also when condition (3) is violated Therefore condition (3) is too restrictive especially for the large problems In concern with the improvement on the makesshypan of 1tl the relative error is 03940 and the proportional improveshyment is 647152 on the average This implies that 647154 of the permutation schedule 1t2 has improved on the initial permutation schedule in its makespan However the quality of improvement made by 1t2 over 1tl is only 03940 in terms of the empirical average-case performance ratio Therefore condition (3) in the CGPS algorithm is more restrictive and the improvement in the average-case performance ratio is so marginal from the empirical point of view
The third experiment concerns the comparison of the CGPS algoshyrithm with the NEH algorithm as well as the CDS algorithm Comparashytive results are shown in Table 3 On basis of the empirical
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ltgtl
ltgtl
Tab
le 2
C
om
para
tiv
e r
esu
lts
for
co
nd
itio
ns
(3)
(7)
an
d (
9)
--------------shy
Job
C
ondi
tion
s E
i R
i Ii
C
1 C
2 C
3 C
4 C
5 C
6 C
7 C
8 -----shy
(3)
59
39
0 0
2 0
0 0
951
220
0
0000
000
00
3 (7
) 59
41
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 59
41
0
0 0
0 0
0 10
000
00
0
0000
NA
-shy
(3)
53
39
4 0
0 0
4 0
829
787
0
3633
500
000
4 (7
) 53
47
0
0 0
0 0
0 10
000
00
0
0000
NA
53
47
0
0 0
0 0
0 10
000
00
0
0000
NA
---shy
-----------shy
(3)
40
44
9 0
1 0
6 0
733
333
0
4365
562
500
5 (7
) 40
60
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 40
eo
0
0 0
0 0
0 10
000
00
0
0000
NA
(3
) 40
4
7
5 0
0 0
8 0
783
333
0
3392
384
600
6 (7
) 40
6
0
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
40
60
0
0 0
0 0
0 10
000
00
0
000
0
NA
-----------shy
(3)
33
37
10
0
0 0
20
0 55
223
9
054
27
33
330
0
7 (7
) 33
6
7
0 0
0 0
0 0
100
0000
000
00
N
A
p (9
) 33
6
7
0 0
0 0
0 0
100
0000
000
00
N
A
0 ------~
(Tab
le 2
Con
td)
gtshy Z
C
)
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Con
diti
ons
~ z
Job
E
i R
i Ii
gt
C
l C
2 C
3 C
4 C
5 C
6 C
7 C
8 S CD
26
40
16
0 1
0 17
0
540
541
0
8209
470
600
CD
0 gt1j
8 (7
) 26
74
0
0 0
0 0
0 10
000
00
0
0000
NA
gt-3 r
(9)
26
74
0 0
0 0
0 0
100
0000
000
00
N
A
ttl
()
36
29
12
0 1
0 22
0
453
125
0
7050
342
900
0 0
9
(7)
36
64
0 0
0 0
0 0
100
0000
000
00
N
A
CD
t
(9)
36
64
0 0
0 0
0 0
100
0000
000
00
N
A
0 ~
0 0(3
) 35
29
19
0
0 0
17
0 44
615
4
079
33
52
780
0
gt-3 r10
(7
) 35
65
0
0 0
0 0
0 10
000
00
0
0000
NA
s
(9
) 35
65
0
0 0
0 0
0 10
000
00
0
0000
NA
(3
) 44
12
20
0
0 0
24
0 21
428
6
054
37
45
450
0
15
(7)
44
56
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
44
56
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
42
9 27
0
0 0
22
0 15
517
2
113
59
55
100
0
20
(7)
42
58
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
42
58
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
53
6 19
0
0 0
22
0 12
766
0
050
21
46
340
0
30
(7)
53
47
0 0
0 0
0 0
100
0000
000
00
N
A
53
47
0 ()
0 0
0 0
100
0000
000
00
N
A
CJ
) C
J)
Of
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w
Job
--~~
~~-
~--
Ri
Ii
w
CIl
Cl
C2
C
3 C
4
C5
C6
C7
C
8 -----------shy
(3)
51
3 21
0
1 0
24
0
61
22
4
0
46
39
456
500
40
(7
) 51
4
9
0 0
0 0
0 0
10
00
00
0
0
00
00
NA
(9
) 51
4
9
0 -----_
_
-shy
0 0
0 --shy-----~~~~
0 0
-----------shy
10
00
00
0
0
00
00
---------shy
NA
(3
) 4
8
4 33
0
0 0
15
0
769
23
0
62
20
68
75
00
50
(7
) 4
8
52
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
48
5
2
0 0
0 0
0 0
100
0000
00
00
0
N
A
51
4 3
7
0 0
0 8
0 8
16
33
05
05
8
8
22
20
0
10
0
(7)
51
49
0
0 0
0 0
0 10
000
00
0
00
00
NA
(9
) 51
4
9
0 0
0 0
0 0
100
0000
00
00
0
N
A
----------shy
(3)
56
0
35
0 0
0 9
0 0
00
00
02
29
1
79
550
0
20
0
(7)
56
4
4
0 0
0 0
0 0
100
0000
00
00
0
N
A
(9)
56
4
4
0 0
0 0
0 0
100
0000
00
00
0
N
A
69
2
24
0
0 0
5 0
64
51
6
0
13
97
82
76
00
30
0
(7)
69
31
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
(9)
69
31
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
(3)
62
1
35
0
0 0
2 0
26
31
6
0
17
96
94
59
00
~
40
0
(7)
62
3
8
0 0
0 0
0 0
10
00
00
0
0
00
00
NA
0
(9)
62
3
8
0 0
0 0
0 0
100
0000
00
00
0
N
A
fja
tJte
2-C
onat
)
gt-3
)shy z Cl
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0gtshy
Con
diti
ons
Job
E
i R
i Ii
C
l C
2 C
3 C
4 C
5 C
6 C
7 C
8 ~ rt
J gt-
lt57
0
38
0 0
0 5
0 0
0000
015
97
88
370
0
rtJ
j
500
(7)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
0 3
(9)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
tr1
tlj
0(3
) 57
1
39
0 0
0 3
0 2
3256
014
97
92
860
0
0 rtJ
60
0
(7)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
~ 57
43
0
0 0
0 0
0 10
000
00
0
0000
NA
0 0
1 34
0
gt-lt
(3)
64
0 0
1 0
277
78
0
1198
971
400
tO
3
700
(7)
64
36
0 0
0 0
0 0
100
0000
000
00
N
A
tr1 ~
(9)
64
36
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
60
2 38
0
0 0
0 0
500
00
0
0999
100
0000
800
(7)
60
40
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
60
40
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
62
0 38
0
0 0
0 0
000
00
0
0999
100
0000
900
(7)
62
38
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
62
38
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
60
0 39
0
0 0
1 0
000
0
010
99
97
500
0
1000
(7
) 60
40
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 60
40
0
0 0
0 0
0 10
000
00
0
0000
NA
w
w
(T
able
2 C
ontd
)
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------
CIJ
Con
diti
ons
CIJ
Job
E
i R
i Ii
O
J
C1
C2
C3
C4
C
5 C
6 C
7
C8
Min
44
615
4
000
00
0
0000
Sm
all
(3
) M
ax
951
220
0
8209
562
500
pro
ble
ms
Avg
66
121
6
050
01
39
021
3
_
Min
0
0000
009
99
45
450
0
Lar
ge
(3
) M
ax
214
286
1
1359
100
0000
p
rob
lem
s A
vg
605
84
0
3374
784
187
--
-_
__
---
Min
0
0000
000
00
0
0000
Ov
eral
l
(3)
Max
95
122
0
113
59
10
000
00
pro
ble
ms
Avg
26
950
0
039
40
64
715
2
tr1
()
gt-3 ~ Q
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gtshyT
able
3
z C
om
par
ativ
e re
sult
s fo
r th
e C
GP
S
CD
S
an
d N
EH
alg
ori
thm
s ~
Job 3
4
5
6
7
8
9
10
15
20
30
40
50
100
200
101
39
101
48
102
64
102
82
102
62
102
7
102
82
102
55
102
43
101
83
101
06
100
85
100
65
100
32
100
17
~CDS
100
96
101
28
102
14
102
32
102
46
102
15
102
04
102
04
101
78
101
53
100
99
100
63
100
55
100
35
100
17
~~--------------
~NElI
100
1
100
39
100
53
100
54
100
48
100
72
100
50
100
47
100
25
100
33
100
08
100
05
100
06
100
02
100
01
(Tab
le 3
Con
td)
lfl
gt-
lfl
0 tJ r t=J
0 0 i
lfl gtshy r 0 0 ~ r $
w
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-----
---
co
jgt
~CGPS
~CDS
~NE
H 0
300
100
07
100
11
100
00
400
100
04
100
06
100
00
500
100
02
100
06
100
00
600
100
02
100
03
100
00
700
100
01
100
02
100
00
800
100
02
100
02
100
00
900
100
01
100
04
100
00
1000
1
0001
1
0003
1
0000
Min
1
0139
1
0096
1
0010
Sm
all
pro
ble
ms
Max
1
0282
1
0246
1
0072
Avg
1
0238
1
0192
1
0047
Min
1
0001
1
0002
1
0000
Lar
ge
pro
ble
ms
Max
1
0243
1
0178
1
0033
Avg
1
0050
1
0042
1
0005
Min
1
0001
1
0002
1
0000
r
Ov
eral
l p
rob
lem
s M
ax
102
82
102
46
100
72
0 -3A
vg
101
15
100
95
100
20
p Z
(Tab
le 3
Con
td)
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lgtT
able
3 (
Con
td)
Z
--------
_shy
SCG
PS
EQ
CG
PS
SCD
S SC
GP
S E
QC
GP
S SN
EH
SN
EH
EQ
IjE
H
SCD
S --
~ Jo
b C
DS
CDS
CG
PS
NEH
N
EH
CG
PS
CD
S CD
S N
EH
en
en
3 0
91
9
0
83
17
12
88
0
0 gtzj
4 2
8
7
11
2
70
28
21
76
3
gt-l
[1j
5 2
82
16
4
58
38
28
66
6
0 0 1
j6
3
77
20
1
51
48
40
56
4
en
7 5
75
20
5
43
52
45
50
5
~ 0 0
8 6
70
24
8
35
57
44
47
9
~
gt-l
9 5
6
7
28
2
40
58
46
46
8
10
7
68
25
5
40
55
42
50
8
15
7
70
23
2
45
53
46
50
4
20
8
65
27
4
46
50
37
58
5
30
8
74
18
0
57
43
35
63
2
40
7
72
21
3
53
44
29
62
9
50
13
62
25
4
49
47
31
59
10
10
0
15
6
0
25
1
52
47
27
71
2
20
0
17
64
19
2
55
43
26
68
6
30
0
12
74
14
7
63
30
17
74
9
w
(T
able
3 C
ontd
)
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Job
S
co
Ps
CD
S E
QC
GP
SC
DS
SCD
S C
GPS
SC
GP
S N
EH
E
QC
GP
SN
EH
S
NE
H
CG
PS
NE
H
SCD
S E
Q
EH
CD
S SC
DS
NE
Il
N
400
14
63
23
5
58
37
14
81
5
500
13
62
25
1
57
42
20
78
2
60
0
9
61
30
4
54
42
12
82
6
700
11
66
23
8
56
36
13
79
8
80
0
10
63
27
4
57
39
14
82
4
900
16
63
21
2
60
38
17
81
2
1000
11
61
28
4
57
39
12
82
6
Sm
all
pro
ble
ms
Min
Max
Avg
0
67
9
7
91
28
375
00
77
125
0
191
250
0
8
337
50
35
17
83
58
525
000
44
1250
12
46
347
500
46
88
598
750
0
9
537
50
Lar
ge
pro
ble
ms
Min
Max
7
17
60
74
14
30
0
8
45
63
30
53
12
46
50
82
2
10
114
00
65
3333
2
326
67
3
4000
546
000
42
0000
233
333
71
333
3
533
33
Ov
eral
l p
rob
lem
s
Min
Max
Avg
0
60
9
17
91
30
873
91
69
434
8 2
182
61
0
8
339
13
35
17
83
58
538
696
42
739
1
12
46
273
043
~~~--~--
46
0
88
10
673
478
5
3478
c 0 ~ gtshy L
0
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343 ANALYSIS OF THE CGPS ALGORITHM
average-case performance ratio the NEH algorithm is the best one folshylowed by the CDS algorithm and then by the CGPS algorithm As comshypared with the CDS algorithm in terms of the proportional superiority 87391 of problem instances better by the CGPS algorithm 694348 of problem instances equal by the CGPS and CDS algorithms and 218261 of problem instances better by the CDS algorithm on the average It follows that the CDS algorithm is better than the CGPS alshygorithm especially for the large problems Similarly the NEH algorithm is better than the CGPS algorithm and the NEH algorithm is better than the CDS algorithm especially for the small problems Therefore we have the same conclusion about these heuristics in terms of the empirishycal average-case performance ratio and proportional superiority
To sum up 503478 ofthe initial permutation schedules generated by the CGPS algorithm are the optimal schedules Among the 496522 of non-optimal schedules 647154 of the permutation schedules 1t2 improve the makespan of the initial permutation schedule However the improvement made by 1t2 is only 03940 over 1tl in terms of the empirical average-case performance ratio Moreover compared with the NEH and CDS algorithms the CGPS algorithm is the worst one in terms of the empirical average-case performance ratio and proportional superiority These imply that the CGPS algorithm is not good enough for applications
5 CONCLUSION
In this paper the 3FSS with minimum makespan criterion is considered Theoretically the heuristic algorithm proposed by Chen et a1 [2] has the best worst-case performance ratio of 53 This paper evaluates the performance of the CGPS algorithm from the empirical perspective Three simulation experiments are performed to analyze its performance in terms of the six evaluation measures on a total of 13800 problems Four conclusions can be drawn from this paper Firstly in the CGPS algorithm 503478 of the initial permutation schedules are the optimal ones 321326 of the permutation schedshyules 1t2 improve the makespan of nl and 175196 of the permutation schedules n2 do not improve their makespan Secondly the empirical average-case performance ratio of initial permutation schedule genershyated by the CGPS algorithm is always smaller than the performance guarantee 53 developed by Chen et al [2] Its empirical average-case performance ratio is in the range of 10011 and 10355 and is 10156 on the average Thirdly the permutation schedule 1t2 improves 1tl in terms of the empirical average-case performance ratio by the non-significant percentage 03940 Fourthly among the three heuristic algorithms the
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344 H C TANG
NEH algorithm is the best one in terms of both empirical average-case performance ratio and proportional superiority followed by the CDS alshygorithm and then by the CGPS algorithm Consequently the CGPS algoshyrithm is not suitable for the 3FSS with minimum makespan criterion
REFERENCES
l H G Campbell R A Dudek and M L Smith A heuristic algorithm for the n-job tn-machine sequencing problem Management Science Vol 16 (1970) pp 630-637
2 B Chen C A Glass C N Potts and V A Strusevich A new heuristic for threeshymachine flow shop scheduling Operations Research Vol 44 (1996) pp 891-898
3 R W Conway W L Maxwell and L W Miller Theory of Scheduling AddisonshyWesley Reading MA 1967
4 R A Dudek S S Panwalkar and M L Smith The lessons of flowshop scheduling research Operations Research Vol 40 (1992) pp 7-13
5 M R Garey D S Johnson and R Sethi The complexity of flowshop and jobshop scheduling Mathematics Operations Research Vol 1 (1976) pp 117-129
6 S M Johnson Optimal two- and three-stage production schedules with setup times included Naval Research Logistic Quarterly Vol 1 (1954) pp 61-68
7 B J Lageweg J K Lenstra and A H G Rinnooy Kan A general bounding scheme for the permutation flow-shop problem Operations Research Vol 26 (1978) pp 53-67
8 E L Lawler J K Lenstra A H G Rinnooy Kan and D B Shmoys Sequencing and Scheduling Algorithms and Complexity in S C Graves A H G Rinnooy
-Kan and P H Zipkin Handbooks in Operations Research and Management Science Vol 4 Logistics of Production and Inventory North Holland Amsterdam 1993 pp 445-522
9 J K Lenstra A H G Rinnooy Kan and P Brucker Complexity of machine scheduling problems Annals Discrete Mathematics Vol 1 (1977) pp 343-362
10 M Nawaz E E Enscore Jr and I Ham A heuristic algorithm for the m-machine n-job flow-shop sequencing problem OMEGA Vol 11 (1983) pp 91-95
II C N Potts An Adaptive branching rule for the permutation flow-shop problem European Journal of Operational Research Vol 5 (1980) pp 19-25
12 A H G Rinnooy Kan Machine scheduling problems classification complexity and computations Nijhoff The Hague 1976
13 H Rock and G Schmidt Machine aggregation heuristics in shop scheduling Methods of Operations Research Vol 45 (1983) pp 303-314
14 C Smutnicki Some results of the worst-case analysis for flow shop scheduling European Journal of Operational Research Vol 109 (1998) pp 66-87
15 S M A Suliman A two-phase heuristic approach to the permutation flow-shop scheduling problem International Journal of Production Economics Vol 64 (2000) pp 143-152
16 H C Tang A new lower bonding rule for permutation flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 249-257
17 H C Tang A modification to the CGPS algorithm for three-machine flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 321-331
Received January 2001
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332 H C TANG
branch-and-bound algorithm with Tangs two-machine lower bound rule [16] The average percentage Popt is 962609 Especially in the case of small problems the branch-and-bound algorithm can always find the optimal schedules within 100000 nodes generated Therefore it is appropriate to adopt the empirical average-ca~e performance ratio to measure the effectiveness of the CGPS algorithm The empirical average-case performance ratio of the initial permutation schedule is in the range of 10011 and 10355 and is 10156 on the average While the empirical average-case performance ratio of the CGPS algorithm is in the range of 10001 and 10282 and is 10115 on the average These values are always smaller than the performance guarantee 53 of the CGPS algorithm
Table 1 Results of simulations for the effectiveness of the COPS
Job P Popt ~1t1
3 59 100 10139 4 53 100 10185 10148 5 40 100 10309 10264 6 40 100 10317 10282 7 33 100 10318 10262 8 26 100 10355 10270 9 36 100 10355 10282 10 35 100 10337 10255 15 44 84 10299 10243 20 42 82 10300 10183 30 53 90 10157 10106 40 51 94 10132 10085 50 48 95 10128 10065 100 51 93 10083 10032 200 56 97 10040 10017 300 69 99 10021 10007 400 62 98 10022 10004 500 57 96 10018 10002 600 57 99 10017 10002 700 64 96 10013 10001 800 60 99 10012 10002
(Table 1 Contd)
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333 ANALYSIS OF THE CGPS ALGORITHM
lOOO 60 97 10001
Small problems Max
Avg 59
402500 lOO lOO
10355 10289
10139 10282 10238
Large problems
Min Max
42 82 10011 10300
10001 10243 10050
Overall problems Max 69
503478 100
922609 10355 10156
10001 10282 10115
The second experiment refers to the comparison of conditions (3) (7) and (9) in terms of the proportional equivalency (14) the relative error (15) and the proportional improvement (16) Table 2 gives the reshysults Notably the number of times of the cases C3 C4 C5 C6 C7 and C8 for conditions (7) and (9) are all zeros In other words the CGPS algorithm with condition (7) or (9) only generates the initial permutashytion schedule It follows that the proportional equivalency is lOO the relative error is zero and the proportional improvement is not availshyable (NA) for each problem These results indicate that the initial pershymutation schedule never violates the worst-case performance ratio of 53 if we adopt the CGPS algorithm with condition (7) or (9)
In the case of condition (3) viz the original CGPS algorithm the proportional equivalency is 2695 on the average This value indicates that 7305 of the desired performance (4) holds also when condition (3) is violated Therefore condition (3) is too restrictive especially for the large problems In concern with the improvement on the makesshypan of 1tl the relative error is 03940 and the proportional improveshyment is 647152 on the average This implies that 647154 of the permutation schedule 1t2 has improved on the initial permutation schedule in its makespan However the quality of improvement made by 1t2 over 1tl is only 03940 in terms of the empirical average-case performance ratio Therefore condition (3) in the CGPS algorithm is more restrictive and the improvement in the average-case performance ratio is so marginal from the empirical point of view
The third experiment concerns the comparison of the CGPS algoshyrithm with the NEH algorithm as well as the CDS algorithm Comparashytive results are shown in Table 3 On basis of the empirical
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ltgtl
ltgtl
Tab
le 2
C
om
para
tiv
e r
esu
lts
for
co
nd
itio
ns
(3)
(7)
an
d (
9)
--------------shy
Job
C
ondi
tion
s E
i R
i Ii
C
1 C
2 C
3 C
4 C
5 C
6 C
7 C
8 -----shy
(3)
59
39
0 0
2 0
0 0
951
220
0
0000
000
00
3 (7
) 59
41
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 59
41
0
0 0
0 0
0 10
000
00
0
0000
NA
-shy
(3)
53
39
4 0
0 0
4 0
829
787
0
3633
500
000
4 (7
) 53
47
0
0 0
0 0
0 10
000
00
0
0000
NA
53
47
0
0 0
0 0
0 10
000
00
0
0000
NA
---shy
-----------shy
(3)
40
44
9 0
1 0
6 0
733
333
0
4365
562
500
5 (7
) 40
60
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 40
eo
0
0 0
0 0
0 10
000
00
0
0000
NA
(3
) 40
4
7
5 0
0 0
8 0
783
333
0
3392
384
600
6 (7
) 40
6
0
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
40
60
0
0 0
0 0
0 10
000
00
0
000
0
NA
-----------shy
(3)
33
37
10
0
0 0
20
0 55
223
9
054
27
33
330
0
7 (7
) 33
6
7
0 0
0 0
0 0
100
0000
000
00
N
A
p (9
) 33
6
7
0 0
0 0
0 0
100
0000
000
00
N
A
0 ------~
(Tab
le 2
Con
td)
gtshy Z
C
)
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Con
diti
ons
~ z
Job
E
i R
i Ii
gt
C
l C
2 C
3 C
4 C
5 C
6 C
7 C
8 S CD
26
40
16
0 1
0 17
0
540
541
0
8209
470
600
CD
0 gt1j
8 (7
) 26
74
0
0 0
0 0
0 10
000
00
0
0000
NA
gt-3 r
(9)
26
74
0 0
0 0
0 0
100
0000
000
00
N
A
ttl
()
36
29
12
0 1
0 22
0
453
125
0
7050
342
900
0 0
9
(7)
36
64
0 0
0 0
0 0
100
0000
000
00
N
A
CD
t
(9)
36
64
0 0
0 0
0 0
100
0000
000
00
N
A
0 ~
0 0(3
) 35
29
19
0
0 0
17
0 44
615
4
079
33
52
780
0
gt-3 r10
(7
) 35
65
0
0 0
0 0
0 10
000
00
0
0000
NA
s
(9
) 35
65
0
0 0
0 0
0 10
000
00
0
0000
NA
(3
) 44
12
20
0
0 0
24
0 21
428
6
054
37
45
450
0
15
(7)
44
56
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
44
56
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
42
9 27
0
0 0
22
0 15
517
2
113
59
55
100
0
20
(7)
42
58
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
42
58
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
53
6 19
0
0 0
22
0 12
766
0
050
21
46
340
0
30
(7)
53
47
0 0
0 0
0 0
100
0000
000
00
N
A
53
47
0 ()
0 0
0 0
100
0000
000
00
N
A
CJ
) C
J)
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w
Job
--~~
~~-
~--
Ri
Ii
w
CIl
Cl
C2
C
3 C
4
C5
C6
C7
C
8 -----------shy
(3)
51
3 21
0
1 0
24
0
61
22
4
0
46
39
456
500
40
(7
) 51
4
9
0 0
0 0
0 0
10
00
00
0
0
00
00
NA
(9
) 51
4
9
0 -----_
_
-shy
0 0
0 --shy-----~~~~
0 0
-----------shy
10
00
00
0
0
00
00
---------shy
NA
(3
) 4
8
4 33
0
0 0
15
0
769
23
0
62
20
68
75
00
50
(7
) 4
8
52
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
48
5
2
0 0
0 0
0 0
100
0000
00
00
0
N
A
51
4 3
7
0 0
0 8
0 8
16
33
05
05
8
8
22
20
0
10
0
(7)
51
49
0
0 0
0 0
0 10
000
00
0
00
00
NA
(9
) 51
4
9
0 0
0 0
0 0
100
0000
00
00
0
N
A
----------shy
(3)
56
0
35
0 0
0 9
0 0
00
00
02
29
1
79
550
0
20
0
(7)
56
4
4
0 0
0 0
0 0
100
0000
00
00
0
N
A
(9)
56
4
4
0 0
0 0
0 0
100
0000
00
00
0
N
A
69
2
24
0
0 0
5 0
64
51
6
0
13
97
82
76
00
30
0
(7)
69
31
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
(9)
69
31
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
(3)
62
1
35
0
0 0
2 0
26
31
6
0
17
96
94
59
00
~
40
0
(7)
62
3
8
0 0
0 0
0 0
10
00
00
0
0
00
00
NA
0
(9)
62
3
8
0 0
0 0
0 0
100
0000
00
00
0
N
A
fja
tJte
2-C
onat
)
gt-3
)shy z Cl
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0gtshy
Con
diti
ons
Job
E
i R
i Ii
C
l C
2 C
3 C
4 C
5 C
6 C
7 C
8 ~ rt
J gt-
lt57
0
38
0 0
0 5
0 0
0000
015
97
88
370
0
rtJ
j
500
(7)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
0 3
(9)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
tr1
tlj
0(3
) 57
1
39
0 0
0 3
0 2
3256
014
97
92
860
0
0 rtJ
60
0
(7)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
~ 57
43
0
0 0
0 0
0 10
000
00
0
0000
NA
0 0
1 34
0
gt-lt
(3)
64
0 0
1 0
277
78
0
1198
971
400
tO
3
700
(7)
64
36
0 0
0 0
0 0
100
0000
000
00
N
A
tr1 ~
(9)
64
36
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
60
2 38
0
0 0
0 0
500
00
0
0999
100
0000
800
(7)
60
40
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
60
40
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
62
0 38
0
0 0
0 0
000
00
0
0999
100
0000
900
(7)
62
38
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
62
38
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
60
0 39
0
0 0
1 0
000
0
010
99
97
500
0
1000
(7
) 60
40
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 60
40
0
0 0
0 0
0 10
000
00
0
0000
NA
w
w
(T
able
2 C
ontd
)
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------
CIJ
Con
diti
ons
CIJ
Job
E
i R
i Ii
O
J
C1
C2
C3
C4
C
5 C
6 C
7
C8
Min
44
615
4
000
00
0
0000
Sm
all
(3
) M
ax
951
220
0
8209
562
500
pro
ble
ms
Avg
66
121
6
050
01
39
021
3
_
Min
0
0000
009
99
45
450
0
Lar
ge
(3
) M
ax
214
286
1
1359
100
0000
p
rob
lem
s A
vg
605
84
0
3374
784
187
--
-_
__
---
Min
0
0000
000
00
0
0000
Ov
eral
l
(3)
Max
95
122
0
113
59
10
000
00
pro
ble
ms
Avg
26
950
0
039
40
64
715
2
tr1
()
gt-3 ~ Q
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gtshyT
able
3
z C
om
par
ativ
e re
sult
s fo
r th
e C
GP
S
CD
S
an
d N
EH
alg
ori
thm
s ~
Job 3
4
5
6
7
8
9
10
15
20
30
40
50
100
200
101
39
101
48
102
64
102
82
102
62
102
7
102
82
102
55
102
43
101
83
101
06
100
85
100
65
100
32
100
17
~CDS
100
96
101
28
102
14
102
32
102
46
102
15
102
04
102
04
101
78
101
53
100
99
100
63
100
55
100
35
100
17
~~--------------
~NElI
100
1
100
39
100
53
100
54
100
48
100
72
100
50
100
47
100
25
100
33
100
08
100
05
100
06
100
02
100
01
(Tab
le 3
Con
td)
lfl
gt-
lfl
0 tJ r t=J
0 0 i
lfl gtshy r 0 0 ~ r $
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-----
---
co
jgt
~CGPS
~CDS
~NE
H 0
300
100
07
100
11
100
00
400
100
04
100
06
100
00
500
100
02
100
06
100
00
600
100
02
100
03
100
00
700
100
01
100
02
100
00
800
100
02
100
02
100
00
900
100
01
100
04
100
00
1000
1
0001
1
0003
1
0000
Min
1
0139
1
0096
1
0010
Sm
all
pro
ble
ms
Max
1
0282
1
0246
1
0072
Avg
1
0238
1
0192
1
0047
Min
1
0001
1
0002
1
0000
Lar
ge
pro
ble
ms
Max
1
0243
1
0178
1
0033
Avg
1
0050
1
0042
1
0005
Min
1
0001
1
0002
1
0000
r
Ov
eral
l p
rob
lem
s M
ax
102
82
102
46
100
72
0 -3A
vg
101
15
100
95
100
20
p Z
(Tab
le 3
Con
td)
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lgtT
able
3 (
Con
td)
Z
--------
_shy
SCG
PS
EQ
CG
PS
SCD
S SC
GP
S E
QC
GP
S SN
EH
SN
EH
EQ
IjE
H
SCD
S --
~ Jo
b C
DS
CDS
CG
PS
NEH
N
EH
CG
PS
CD
S CD
S N
EH
en
en
3 0
91
9
0
83
17
12
88
0
0 gtzj
4 2
8
7
11
2
70
28
21
76
3
gt-l
[1j
5 2
82
16
4
58
38
28
66
6
0 0 1
j6
3
77
20
1
51
48
40
56
4
en
7 5
75
20
5
43
52
45
50
5
~ 0 0
8 6
70
24
8
35
57
44
47
9
~
gt-l
9 5
6
7
28
2
40
58
46
46
8
10
7
68
25
5
40
55
42
50
8
15
7
70
23
2
45
53
46
50
4
20
8
65
27
4
46
50
37
58
5
30
8
74
18
0
57
43
35
63
2
40
7
72
21
3
53
44
29
62
9
50
13
62
25
4
49
47
31
59
10
10
0
15
6
0
25
1
52
47
27
71
2
20
0
17
64
19
2
55
43
26
68
6
30
0
12
74
14
7
63
30
17
74
9
w
(T
able
3 C
ontd
)
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Job
S
co
Ps
CD
S E
QC
GP
SC
DS
SCD
S C
GPS
SC
GP
S N
EH
E
QC
GP
SN
EH
S
NE
H
CG
PS
NE
H
SCD
S E
Q
EH
CD
S SC
DS
NE
Il
N
400
14
63
23
5
58
37
14
81
5
500
13
62
25
1
57
42
20
78
2
60
0
9
61
30
4
54
42
12
82
6
700
11
66
23
8
56
36
13
79
8
80
0
10
63
27
4
57
39
14
82
4
900
16
63
21
2
60
38
17
81
2
1000
11
61
28
4
57
39
12
82
6
Sm
all
pro
ble
ms
Min
Max
Avg
0
67
9
7
91
28
375
00
77
125
0
191
250
0
8
337
50
35
17
83
58
525
000
44
1250
12
46
347
500
46
88
598
750
0
9
537
50
Lar
ge
pro
ble
ms
Min
Max
7
17
60
74
14
30
0
8
45
63
30
53
12
46
50
82
2
10
114
00
65
3333
2
326
67
3
4000
546
000
42
0000
233
333
71
333
3
533
33
Ov
eral
l p
rob
lem
s
Min
Max
Avg
0
60
9
17
91
30
873
91
69
434
8 2
182
61
0
8
339
13
35
17
83
58
538
696
42
739
1
12
46
273
043
~~~--~--
46
0
88
10
673
478
5
3478
c 0 ~ gtshy L
0
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343 ANALYSIS OF THE CGPS ALGORITHM
average-case performance ratio the NEH algorithm is the best one folshylowed by the CDS algorithm and then by the CGPS algorithm As comshypared with the CDS algorithm in terms of the proportional superiority 87391 of problem instances better by the CGPS algorithm 694348 of problem instances equal by the CGPS and CDS algorithms and 218261 of problem instances better by the CDS algorithm on the average It follows that the CDS algorithm is better than the CGPS alshygorithm especially for the large problems Similarly the NEH algorithm is better than the CGPS algorithm and the NEH algorithm is better than the CDS algorithm especially for the small problems Therefore we have the same conclusion about these heuristics in terms of the empirishycal average-case performance ratio and proportional superiority
To sum up 503478 ofthe initial permutation schedules generated by the CGPS algorithm are the optimal schedules Among the 496522 of non-optimal schedules 647154 of the permutation schedules 1t2 improve the makespan of the initial permutation schedule However the improvement made by 1t2 is only 03940 over 1tl in terms of the empirical average-case performance ratio Moreover compared with the NEH and CDS algorithms the CGPS algorithm is the worst one in terms of the empirical average-case performance ratio and proportional superiority These imply that the CGPS algorithm is not good enough for applications
5 CONCLUSION
In this paper the 3FSS with minimum makespan criterion is considered Theoretically the heuristic algorithm proposed by Chen et a1 [2] has the best worst-case performance ratio of 53 This paper evaluates the performance of the CGPS algorithm from the empirical perspective Three simulation experiments are performed to analyze its performance in terms of the six evaluation measures on a total of 13800 problems Four conclusions can be drawn from this paper Firstly in the CGPS algorithm 503478 of the initial permutation schedules are the optimal ones 321326 of the permutation schedshyules 1t2 improve the makespan of nl and 175196 of the permutation schedules n2 do not improve their makespan Secondly the empirical average-case performance ratio of initial permutation schedule genershyated by the CGPS algorithm is always smaller than the performance guarantee 53 developed by Chen et al [2] Its empirical average-case performance ratio is in the range of 10011 and 10355 and is 10156 on the average Thirdly the permutation schedule 1t2 improves 1tl in terms of the empirical average-case performance ratio by the non-significant percentage 03940 Fourthly among the three heuristic algorithms the
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344 H C TANG
NEH algorithm is the best one in terms of both empirical average-case performance ratio and proportional superiority followed by the CDS alshygorithm and then by the CGPS algorithm Consequently the CGPS algoshyrithm is not suitable for the 3FSS with minimum makespan criterion
REFERENCES
l H G Campbell R A Dudek and M L Smith A heuristic algorithm for the n-job tn-machine sequencing problem Management Science Vol 16 (1970) pp 630-637
2 B Chen C A Glass C N Potts and V A Strusevich A new heuristic for threeshymachine flow shop scheduling Operations Research Vol 44 (1996) pp 891-898
3 R W Conway W L Maxwell and L W Miller Theory of Scheduling AddisonshyWesley Reading MA 1967
4 R A Dudek S S Panwalkar and M L Smith The lessons of flowshop scheduling research Operations Research Vol 40 (1992) pp 7-13
5 M R Garey D S Johnson and R Sethi The complexity of flowshop and jobshop scheduling Mathematics Operations Research Vol 1 (1976) pp 117-129
6 S M Johnson Optimal two- and three-stage production schedules with setup times included Naval Research Logistic Quarterly Vol 1 (1954) pp 61-68
7 B J Lageweg J K Lenstra and A H G Rinnooy Kan A general bounding scheme for the permutation flow-shop problem Operations Research Vol 26 (1978) pp 53-67
8 E L Lawler J K Lenstra A H G Rinnooy Kan and D B Shmoys Sequencing and Scheduling Algorithms and Complexity in S C Graves A H G Rinnooy
-Kan and P H Zipkin Handbooks in Operations Research and Management Science Vol 4 Logistics of Production and Inventory North Holland Amsterdam 1993 pp 445-522
9 J K Lenstra A H G Rinnooy Kan and P Brucker Complexity of machine scheduling problems Annals Discrete Mathematics Vol 1 (1977) pp 343-362
10 M Nawaz E E Enscore Jr and I Ham A heuristic algorithm for the m-machine n-job flow-shop sequencing problem OMEGA Vol 11 (1983) pp 91-95
II C N Potts An Adaptive branching rule for the permutation flow-shop problem European Journal of Operational Research Vol 5 (1980) pp 19-25
12 A H G Rinnooy Kan Machine scheduling problems classification complexity and computations Nijhoff The Hague 1976
13 H Rock and G Schmidt Machine aggregation heuristics in shop scheduling Methods of Operations Research Vol 45 (1983) pp 303-314
14 C Smutnicki Some results of the worst-case analysis for flow shop scheduling European Journal of Operational Research Vol 109 (1998) pp 66-87
15 S M A Suliman A two-phase heuristic approach to the permutation flow-shop scheduling problem International Journal of Production Economics Vol 64 (2000) pp 143-152
16 H C Tang A new lower bonding rule for permutation flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 249-257
17 H C Tang A modification to the CGPS algorithm for three-machine flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 321-331
Received January 2001
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333 ANALYSIS OF THE CGPS ALGORITHM
lOOO 60 97 10001
Small problems Max
Avg 59
402500 lOO lOO
10355 10289
10139 10282 10238
Large problems
Min Max
42 82 10011 10300
10001 10243 10050
Overall problems Max 69
503478 100
922609 10355 10156
10001 10282 10115
The second experiment refers to the comparison of conditions (3) (7) and (9) in terms of the proportional equivalency (14) the relative error (15) and the proportional improvement (16) Table 2 gives the reshysults Notably the number of times of the cases C3 C4 C5 C6 C7 and C8 for conditions (7) and (9) are all zeros In other words the CGPS algorithm with condition (7) or (9) only generates the initial permutashytion schedule It follows that the proportional equivalency is lOO the relative error is zero and the proportional improvement is not availshyable (NA) for each problem These results indicate that the initial pershymutation schedule never violates the worst-case performance ratio of 53 if we adopt the CGPS algorithm with condition (7) or (9)
In the case of condition (3) viz the original CGPS algorithm the proportional equivalency is 2695 on the average This value indicates that 7305 of the desired performance (4) holds also when condition (3) is violated Therefore condition (3) is too restrictive especially for the large problems In concern with the improvement on the makesshypan of 1tl the relative error is 03940 and the proportional improveshyment is 647152 on the average This implies that 647154 of the permutation schedule 1t2 has improved on the initial permutation schedule in its makespan However the quality of improvement made by 1t2 over 1tl is only 03940 in terms of the empirical average-case performance ratio Therefore condition (3) in the CGPS algorithm is more restrictive and the improvement in the average-case performance ratio is so marginal from the empirical point of view
The third experiment concerns the comparison of the CGPS algoshyrithm with the NEH algorithm as well as the CDS algorithm Comparashytive results are shown in Table 3 On basis of the empirical
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ltgtl
ltgtl
Tab
le 2
C
om
para
tiv
e r
esu
lts
for
co
nd
itio
ns
(3)
(7)
an
d (
9)
--------------shy
Job
C
ondi
tion
s E
i R
i Ii
C
1 C
2 C
3 C
4 C
5 C
6 C
7 C
8 -----shy
(3)
59
39
0 0
2 0
0 0
951
220
0
0000
000
00
3 (7
) 59
41
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 59
41
0
0 0
0 0
0 10
000
00
0
0000
NA
-shy
(3)
53
39
4 0
0 0
4 0
829
787
0
3633
500
000
4 (7
) 53
47
0
0 0
0 0
0 10
000
00
0
0000
NA
53
47
0
0 0
0 0
0 10
000
00
0
0000
NA
---shy
-----------shy
(3)
40
44
9 0
1 0
6 0
733
333
0
4365
562
500
5 (7
) 40
60
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 40
eo
0
0 0
0 0
0 10
000
00
0
0000
NA
(3
) 40
4
7
5 0
0 0
8 0
783
333
0
3392
384
600
6 (7
) 40
6
0
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
40
60
0
0 0
0 0
0 10
000
00
0
000
0
NA
-----------shy
(3)
33
37
10
0
0 0
20
0 55
223
9
054
27
33
330
0
7 (7
) 33
6
7
0 0
0 0
0 0
100
0000
000
00
N
A
p (9
) 33
6
7
0 0
0 0
0 0
100
0000
000
00
N
A
0 ------~
(Tab
le 2
Con
td)
gtshy Z
C
)
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Con
diti
ons
~ z
Job
E
i R
i Ii
gt
C
l C
2 C
3 C
4 C
5 C
6 C
7 C
8 S CD
26
40
16
0 1
0 17
0
540
541
0
8209
470
600
CD
0 gt1j
8 (7
) 26
74
0
0 0
0 0
0 10
000
00
0
0000
NA
gt-3 r
(9)
26
74
0 0
0 0
0 0
100
0000
000
00
N
A
ttl
()
36
29
12
0 1
0 22
0
453
125
0
7050
342
900
0 0
9
(7)
36
64
0 0
0 0
0 0
100
0000
000
00
N
A
CD
t
(9)
36
64
0 0
0 0
0 0
100
0000
000
00
N
A
0 ~
0 0(3
) 35
29
19
0
0 0
17
0 44
615
4
079
33
52
780
0
gt-3 r10
(7
) 35
65
0
0 0
0 0
0 10
000
00
0
0000
NA
s
(9
) 35
65
0
0 0
0 0
0 10
000
00
0
0000
NA
(3
) 44
12
20
0
0 0
24
0 21
428
6
054
37
45
450
0
15
(7)
44
56
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
44
56
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
42
9 27
0
0 0
22
0 15
517
2
113
59
55
100
0
20
(7)
42
58
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
42
58
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
53
6 19
0
0 0
22
0 12
766
0
050
21
46
340
0
30
(7)
53
47
0 0
0 0
0 0
100
0000
000
00
N
A
53
47
0 ()
0 0
0 0
100
0000
000
00
N
A
CJ
) C
J)
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w
Job
--~~
~~-
~--
Ri
Ii
w
CIl
Cl
C2
C
3 C
4
C5
C6
C7
C
8 -----------shy
(3)
51
3 21
0
1 0
24
0
61
22
4
0
46
39
456
500
40
(7
) 51
4
9
0 0
0 0
0 0
10
00
00
0
0
00
00
NA
(9
) 51
4
9
0 -----_
_
-shy
0 0
0 --shy-----~~~~
0 0
-----------shy
10
00
00
0
0
00
00
---------shy
NA
(3
) 4
8
4 33
0
0 0
15
0
769
23
0
62
20
68
75
00
50
(7
) 4
8
52
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
48
5
2
0 0
0 0
0 0
100
0000
00
00
0
N
A
51
4 3
7
0 0
0 8
0 8
16
33
05
05
8
8
22
20
0
10
0
(7)
51
49
0
0 0
0 0
0 10
000
00
0
00
00
NA
(9
) 51
4
9
0 0
0 0
0 0
100
0000
00
00
0
N
A
----------shy
(3)
56
0
35
0 0
0 9
0 0
00
00
02
29
1
79
550
0
20
0
(7)
56
4
4
0 0
0 0
0 0
100
0000
00
00
0
N
A
(9)
56
4
4
0 0
0 0
0 0
100
0000
00
00
0
N
A
69
2
24
0
0 0
5 0
64
51
6
0
13
97
82
76
00
30
0
(7)
69
31
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
(9)
69
31
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
(3)
62
1
35
0
0 0
2 0
26
31
6
0
17
96
94
59
00
~
40
0
(7)
62
3
8
0 0
0 0
0 0
10
00
00
0
0
00
00
NA
0
(9)
62
3
8
0 0
0 0
0 0
100
0000
00
00
0
N
A
fja
tJte
2-C
onat
)
gt-3
)shy z Cl
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0gtshy
Con
diti
ons
Job
E
i R
i Ii
C
l C
2 C
3 C
4 C
5 C
6 C
7 C
8 ~ rt
J gt-
lt57
0
38
0 0
0 5
0 0
0000
015
97
88
370
0
rtJ
j
500
(7)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
0 3
(9)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
tr1
tlj
0(3
) 57
1
39
0 0
0 3
0 2
3256
014
97
92
860
0
0 rtJ
60
0
(7)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
~ 57
43
0
0 0
0 0
0 10
000
00
0
0000
NA
0 0
1 34
0
gt-lt
(3)
64
0 0
1 0
277
78
0
1198
971
400
tO
3
700
(7)
64
36
0 0
0 0
0 0
100
0000
000
00
N
A
tr1 ~
(9)
64
36
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
60
2 38
0
0 0
0 0
500
00
0
0999
100
0000
800
(7)
60
40
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
60
40
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
62
0 38
0
0 0
0 0
000
00
0
0999
100
0000
900
(7)
62
38
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
62
38
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
60
0 39
0
0 0
1 0
000
0
010
99
97
500
0
1000
(7
) 60
40
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 60
40
0
0 0
0 0
0 10
000
00
0
0000
NA
w
w
(T
able
2 C
ontd
)
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------
CIJ
Con
diti
ons
CIJ
Job
E
i R
i Ii
O
J
C1
C2
C3
C4
C
5 C
6 C
7
C8
Min
44
615
4
000
00
0
0000
Sm
all
(3
) M
ax
951
220
0
8209
562
500
pro
ble
ms
Avg
66
121
6
050
01
39
021
3
_
Min
0
0000
009
99
45
450
0
Lar
ge
(3
) M
ax
214
286
1
1359
100
0000
p
rob
lem
s A
vg
605
84
0
3374
784
187
--
-_
__
---
Min
0
0000
000
00
0
0000
Ov
eral
l
(3)
Max
95
122
0
113
59
10
000
00
pro
ble
ms
Avg
26
950
0
039
40
64
715
2
tr1
()
gt-3 ~ Q
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gtshyT
able
3
z C
om
par
ativ
e re
sult
s fo
r th
e C
GP
S
CD
S
an
d N
EH
alg
ori
thm
s ~
Job 3
4
5
6
7
8
9
10
15
20
30
40
50
100
200
101
39
101
48
102
64
102
82
102
62
102
7
102
82
102
55
102
43
101
83
101
06
100
85
100
65
100
32
100
17
~CDS
100
96
101
28
102
14
102
32
102
46
102
15
102
04
102
04
101
78
101
53
100
99
100
63
100
55
100
35
100
17
~~--------------
~NElI
100
1
100
39
100
53
100
54
100
48
100
72
100
50
100
47
100
25
100
33
100
08
100
05
100
06
100
02
100
01
(Tab
le 3
Con
td)
lfl
gt-
lfl
0 tJ r t=J
0 0 i
lfl gtshy r 0 0 ~ r $
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-----
---
co
jgt
~CGPS
~CDS
~NE
H 0
300
100
07
100
11
100
00
400
100
04
100
06
100
00
500
100
02
100
06
100
00
600
100
02
100
03
100
00
700
100
01
100
02
100
00
800
100
02
100
02
100
00
900
100
01
100
04
100
00
1000
1
0001
1
0003
1
0000
Min
1
0139
1
0096
1
0010
Sm
all
pro
ble
ms
Max
1
0282
1
0246
1
0072
Avg
1
0238
1
0192
1
0047
Min
1
0001
1
0002
1
0000
Lar
ge
pro
ble
ms
Max
1
0243
1
0178
1
0033
Avg
1
0050
1
0042
1
0005
Min
1
0001
1
0002
1
0000
r
Ov
eral
l p
rob
lem
s M
ax
102
82
102
46
100
72
0 -3A
vg
101
15
100
95
100
20
p Z
(Tab
le 3
Con
td)
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lgtT
able
3 (
Con
td)
Z
--------
_shy
SCG
PS
EQ
CG
PS
SCD
S SC
GP
S E
QC
GP
S SN
EH
SN
EH
EQ
IjE
H
SCD
S --
~ Jo
b C
DS
CDS
CG
PS
NEH
N
EH
CG
PS
CD
S CD
S N
EH
en
en
3 0
91
9
0
83
17
12
88
0
0 gtzj
4 2
8
7
11
2
70
28
21
76
3
gt-l
[1j
5 2
82
16
4
58
38
28
66
6
0 0 1
j6
3
77
20
1
51
48
40
56
4
en
7 5
75
20
5
43
52
45
50
5
~ 0 0
8 6
70
24
8
35
57
44
47
9
~
gt-l
9 5
6
7
28
2
40
58
46
46
8
10
7
68
25
5
40
55
42
50
8
15
7
70
23
2
45
53
46
50
4
20
8
65
27
4
46
50
37
58
5
30
8
74
18
0
57
43
35
63
2
40
7
72
21
3
53
44
29
62
9
50
13
62
25
4
49
47
31
59
10
10
0
15
6
0
25
1
52
47
27
71
2
20
0
17
64
19
2
55
43
26
68
6
30
0
12
74
14
7
63
30
17
74
9
w
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able
3 C
ontd
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Job
S
co
Ps
CD
S E
QC
GP
SC
DS
SCD
S C
GPS
SC
GP
S N
EH
E
QC
GP
SN
EH
S
NE
H
CG
PS
NE
H
SCD
S E
Q
EH
CD
S SC
DS
NE
Il
N
400
14
63
23
5
58
37
14
81
5
500
13
62
25
1
57
42
20
78
2
60
0
9
61
30
4
54
42
12
82
6
700
11
66
23
8
56
36
13
79
8
80
0
10
63
27
4
57
39
14
82
4
900
16
63
21
2
60
38
17
81
2
1000
11
61
28
4
57
39
12
82
6
Sm
all
pro
ble
ms
Min
Max
Avg
0
67
9
7
91
28
375
00
77
125
0
191
250
0
8
337
50
35
17
83
58
525
000
44
1250
12
46
347
500
46
88
598
750
0
9
537
50
Lar
ge
pro
ble
ms
Min
Max
7
17
60
74
14
30
0
8
45
63
30
53
12
46
50
82
2
10
114
00
65
3333
2
326
67
3
4000
546
000
42
0000
233
333
71
333
3
533
33
Ov
eral
l p
rob
lem
s
Min
Max
Avg
0
60
9
17
91
30
873
91
69
434
8 2
182
61
0
8
339
13
35
17
83
58
538
696
42
739
1
12
46
273
043
~~~--~--
46
0
88
10
673
478
5
3478
c 0 ~ gtshy L
0
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343 ANALYSIS OF THE CGPS ALGORITHM
average-case performance ratio the NEH algorithm is the best one folshylowed by the CDS algorithm and then by the CGPS algorithm As comshypared with the CDS algorithm in terms of the proportional superiority 87391 of problem instances better by the CGPS algorithm 694348 of problem instances equal by the CGPS and CDS algorithms and 218261 of problem instances better by the CDS algorithm on the average It follows that the CDS algorithm is better than the CGPS alshygorithm especially for the large problems Similarly the NEH algorithm is better than the CGPS algorithm and the NEH algorithm is better than the CDS algorithm especially for the small problems Therefore we have the same conclusion about these heuristics in terms of the empirishycal average-case performance ratio and proportional superiority
To sum up 503478 ofthe initial permutation schedules generated by the CGPS algorithm are the optimal schedules Among the 496522 of non-optimal schedules 647154 of the permutation schedules 1t2 improve the makespan of the initial permutation schedule However the improvement made by 1t2 is only 03940 over 1tl in terms of the empirical average-case performance ratio Moreover compared with the NEH and CDS algorithms the CGPS algorithm is the worst one in terms of the empirical average-case performance ratio and proportional superiority These imply that the CGPS algorithm is not good enough for applications
5 CONCLUSION
In this paper the 3FSS with minimum makespan criterion is considered Theoretically the heuristic algorithm proposed by Chen et a1 [2] has the best worst-case performance ratio of 53 This paper evaluates the performance of the CGPS algorithm from the empirical perspective Three simulation experiments are performed to analyze its performance in terms of the six evaluation measures on a total of 13800 problems Four conclusions can be drawn from this paper Firstly in the CGPS algorithm 503478 of the initial permutation schedules are the optimal ones 321326 of the permutation schedshyules 1t2 improve the makespan of nl and 175196 of the permutation schedules n2 do not improve their makespan Secondly the empirical average-case performance ratio of initial permutation schedule genershyated by the CGPS algorithm is always smaller than the performance guarantee 53 developed by Chen et al [2] Its empirical average-case performance ratio is in the range of 10011 and 10355 and is 10156 on the average Thirdly the permutation schedule 1t2 improves 1tl in terms of the empirical average-case performance ratio by the non-significant percentage 03940 Fourthly among the three heuristic algorithms the
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344 H C TANG
NEH algorithm is the best one in terms of both empirical average-case performance ratio and proportional superiority followed by the CDS alshygorithm and then by the CGPS algorithm Consequently the CGPS algoshyrithm is not suitable for the 3FSS with minimum makespan criterion
REFERENCES
l H G Campbell R A Dudek and M L Smith A heuristic algorithm for the n-job tn-machine sequencing problem Management Science Vol 16 (1970) pp 630-637
2 B Chen C A Glass C N Potts and V A Strusevich A new heuristic for threeshymachine flow shop scheduling Operations Research Vol 44 (1996) pp 891-898
3 R W Conway W L Maxwell and L W Miller Theory of Scheduling AddisonshyWesley Reading MA 1967
4 R A Dudek S S Panwalkar and M L Smith The lessons of flowshop scheduling research Operations Research Vol 40 (1992) pp 7-13
5 M R Garey D S Johnson and R Sethi The complexity of flowshop and jobshop scheduling Mathematics Operations Research Vol 1 (1976) pp 117-129
6 S M Johnson Optimal two- and three-stage production schedules with setup times included Naval Research Logistic Quarterly Vol 1 (1954) pp 61-68
7 B J Lageweg J K Lenstra and A H G Rinnooy Kan A general bounding scheme for the permutation flow-shop problem Operations Research Vol 26 (1978) pp 53-67
8 E L Lawler J K Lenstra A H G Rinnooy Kan and D B Shmoys Sequencing and Scheduling Algorithms and Complexity in S C Graves A H G Rinnooy
-Kan and P H Zipkin Handbooks in Operations Research and Management Science Vol 4 Logistics of Production and Inventory North Holland Amsterdam 1993 pp 445-522
9 J K Lenstra A H G Rinnooy Kan and P Brucker Complexity of machine scheduling problems Annals Discrete Mathematics Vol 1 (1977) pp 343-362
10 M Nawaz E E Enscore Jr and I Ham A heuristic algorithm for the m-machine n-job flow-shop sequencing problem OMEGA Vol 11 (1983) pp 91-95
II C N Potts An Adaptive branching rule for the permutation flow-shop problem European Journal of Operational Research Vol 5 (1980) pp 19-25
12 A H G Rinnooy Kan Machine scheduling problems classification complexity and computations Nijhoff The Hague 1976
13 H Rock and G Schmidt Machine aggregation heuristics in shop scheduling Methods of Operations Research Vol 45 (1983) pp 303-314
14 C Smutnicki Some results of the worst-case analysis for flow shop scheduling European Journal of Operational Research Vol 109 (1998) pp 66-87
15 S M A Suliman A two-phase heuristic approach to the permutation flow-shop scheduling problem International Journal of Production Economics Vol 64 (2000) pp 143-152
16 H C Tang A new lower bonding rule for permutation flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 249-257
17 H C Tang A modification to the CGPS algorithm for three-machine flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 321-331
Received January 2001
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ltgtl
ltgtl
Tab
le 2
C
om
para
tiv
e r
esu
lts
for
co
nd
itio
ns
(3)
(7)
an
d (
9)
--------------shy
Job
C
ondi
tion
s E
i R
i Ii
C
1 C
2 C
3 C
4 C
5 C
6 C
7 C
8 -----shy
(3)
59
39
0 0
2 0
0 0
951
220
0
0000
000
00
3 (7
) 59
41
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 59
41
0
0 0
0 0
0 10
000
00
0
0000
NA
-shy
(3)
53
39
4 0
0 0
4 0
829
787
0
3633
500
000
4 (7
) 53
47
0
0 0
0 0
0 10
000
00
0
0000
NA
53
47
0
0 0
0 0
0 10
000
00
0
0000
NA
---shy
-----------shy
(3)
40
44
9 0
1 0
6 0
733
333
0
4365
562
500
5 (7
) 40
60
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 40
eo
0
0 0
0 0
0 10
000
00
0
0000
NA
(3
) 40
4
7
5 0
0 0
8 0
783
333
0
3392
384
600
6 (7
) 40
6
0
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
40
60
0
0 0
0 0
0 10
000
00
0
000
0
NA
-----------shy
(3)
33
37
10
0
0 0
20
0 55
223
9
054
27
33
330
0
7 (7
) 33
6
7
0 0
0 0
0 0
100
0000
000
00
N
A
p (9
) 33
6
7
0 0
0 0
0 0
100
0000
000
00
N
A
0 ------~
(Tab
le 2
Con
td)
gtshy Z
C
)
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Con
diti
ons
~ z
Job
E
i R
i Ii
gt
C
l C
2 C
3 C
4 C
5 C
6 C
7 C
8 S CD
26
40
16
0 1
0 17
0
540
541
0
8209
470
600
CD
0 gt1j
8 (7
) 26
74
0
0 0
0 0
0 10
000
00
0
0000
NA
gt-3 r
(9)
26
74
0 0
0 0
0 0
100
0000
000
00
N
A
ttl
()
36
29
12
0 1
0 22
0
453
125
0
7050
342
900
0 0
9
(7)
36
64
0 0
0 0
0 0
100
0000
000
00
N
A
CD
t
(9)
36
64
0 0
0 0
0 0
100
0000
000
00
N
A
0 ~
0 0(3
) 35
29
19
0
0 0
17
0 44
615
4
079
33
52
780
0
gt-3 r10
(7
) 35
65
0
0 0
0 0
0 10
000
00
0
0000
NA
s
(9
) 35
65
0
0 0
0 0
0 10
000
00
0
0000
NA
(3
) 44
12
20
0
0 0
24
0 21
428
6
054
37
45
450
0
15
(7)
44
56
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
44
56
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
42
9 27
0
0 0
22
0 15
517
2
113
59
55
100
0
20
(7)
42
58
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
42
58
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
53
6 19
0
0 0
22
0 12
766
0
050
21
46
340
0
30
(7)
53
47
0 0
0 0
0 0
100
0000
000
00
N
A
53
47
0 ()
0 0
0 0
100
0000
000
00
N
A
CJ
) C
J)
Of
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w
Job
--~~
~~-
~--
Ri
Ii
w
CIl
Cl
C2
C
3 C
4
C5
C6
C7
C
8 -----------shy
(3)
51
3 21
0
1 0
24
0
61
22
4
0
46
39
456
500
40
(7
) 51
4
9
0 0
0 0
0 0
10
00
00
0
0
00
00
NA
(9
) 51
4
9
0 -----_
_
-shy
0 0
0 --shy-----~~~~
0 0
-----------shy
10
00
00
0
0
00
00
---------shy
NA
(3
) 4
8
4 33
0
0 0
15
0
769
23
0
62
20
68
75
00
50
(7
) 4
8
52
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
48
5
2
0 0
0 0
0 0
100
0000
00
00
0
N
A
51
4 3
7
0 0
0 8
0 8
16
33
05
05
8
8
22
20
0
10
0
(7)
51
49
0
0 0
0 0
0 10
000
00
0
00
00
NA
(9
) 51
4
9
0 0
0 0
0 0
100
0000
00
00
0
N
A
----------shy
(3)
56
0
35
0 0
0 9
0 0
00
00
02
29
1
79
550
0
20
0
(7)
56
4
4
0 0
0 0
0 0
100
0000
00
00
0
N
A
(9)
56
4
4
0 0
0 0
0 0
100
0000
00
00
0
N
A
69
2
24
0
0 0
5 0
64
51
6
0
13
97
82
76
00
30
0
(7)
69
31
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
(9)
69
31
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
(3)
62
1
35
0
0 0
2 0
26
31
6
0
17
96
94
59
00
~
40
0
(7)
62
3
8
0 0
0 0
0 0
10
00
00
0
0
00
00
NA
0
(9)
62
3
8
0 0
0 0
0 0
100
0000
00
00
0
N
A
fja
tJte
2-C
onat
)
gt-3
)shy z Cl
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0gtshy
Con
diti
ons
Job
E
i R
i Ii
C
l C
2 C
3 C
4 C
5 C
6 C
7 C
8 ~ rt
J gt-
lt57
0
38
0 0
0 5
0 0
0000
015
97
88
370
0
rtJ
j
500
(7)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
0 3
(9)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
tr1
tlj
0(3
) 57
1
39
0 0
0 3
0 2
3256
014
97
92
860
0
0 rtJ
60
0
(7)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
~ 57
43
0
0 0
0 0
0 10
000
00
0
0000
NA
0 0
1 34
0
gt-lt
(3)
64
0 0
1 0
277
78
0
1198
971
400
tO
3
700
(7)
64
36
0 0
0 0
0 0
100
0000
000
00
N
A
tr1 ~
(9)
64
36
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
60
2 38
0
0 0
0 0
500
00
0
0999
100
0000
800
(7)
60
40
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
60
40
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
62
0 38
0
0 0
0 0
000
00
0
0999
100
0000
900
(7)
62
38
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
62
38
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
60
0 39
0
0 0
1 0
000
0
010
99
97
500
0
1000
(7
) 60
40
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 60
40
0
0 0
0 0
0 10
000
00
0
0000
NA
w
w
(T
able
2 C
ontd
)
-l
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------
CIJ
Con
diti
ons
CIJ
Job
E
i R
i Ii
O
J
C1
C2
C3
C4
C
5 C
6 C
7
C8
Min
44
615
4
000
00
0
0000
Sm
all
(3
) M
ax
951
220
0
8209
562
500
pro
ble
ms
Avg
66
121
6
050
01
39
021
3
_
Min
0
0000
009
99
45
450
0
Lar
ge
(3
) M
ax
214
286
1
1359
100
0000
p
rob
lem
s A
vg
605
84
0
3374
784
187
--
-_
__
---
Min
0
0000
000
00
0
0000
Ov
eral
l
(3)
Max
95
122
0
113
59
10
000
00
pro
ble
ms
Avg
26
950
0
039
40
64
715
2
tr1
()
gt-3 ~ Q
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gtshyT
able
3
z C
om
par
ativ
e re
sult
s fo
r th
e C
GP
S
CD
S
an
d N
EH
alg
ori
thm
s ~
Job 3
4
5
6
7
8
9
10
15
20
30
40
50
100
200
101
39
101
48
102
64
102
82
102
62
102
7
102
82
102
55
102
43
101
83
101
06
100
85
100
65
100
32
100
17
~CDS
100
96
101
28
102
14
102
32
102
46
102
15
102
04
102
04
101
78
101
53
100
99
100
63
100
55
100
35
100
17
~~--------------
~NElI
100
1
100
39
100
53
100
54
100
48
100
72
100
50
100
47
100
25
100
33
100
08
100
05
100
06
100
02
100
01
(Tab
le 3
Con
td)
lfl
gt-
lfl
0 tJ r t=J
0 0 i
lfl gtshy r 0 0 ~ r $
w
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to
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-----
---
co
jgt
~CGPS
~CDS
~NE
H 0
300
100
07
100
11
100
00
400
100
04
100
06
100
00
500
100
02
100
06
100
00
600
100
02
100
03
100
00
700
100
01
100
02
100
00
800
100
02
100
02
100
00
900
100
01
100
04
100
00
1000
1
0001
1
0003
1
0000
Min
1
0139
1
0096
1
0010
Sm
all
pro
ble
ms
Max
1
0282
1
0246
1
0072
Avg
1
0238
1
0192
1
0047
Min
1
0001
1
0002
1
0000
Lar
ge
pro
ble
ms
Max
1
0243
1
0178
1
0033
Avg
1
0050
1
0042
1
0005
Min
1
0001
1
0002
1
0000
r
Ov
eral
l p
rob
lem
s M
ax
102
82
102
46
100
72
0 -3A
vg
101
15
100
95
100
20
p Z
(Tab
le 3
Con
td)
0
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lgtT
able
3 (
Con
td)
Z
--------
_shy
SCG
PS
EQ
CG
PS
SCD
S SC
GP
S E
QC
GP
S SN
EH
SN
EH
EQ
IjE
H
SCD
S --
~ Jo
b C
DS
CDS
CG
PS
NEH
N
EH
CG
PS
CD
S CD
S N
EH
en
en
3 0
91
9
0
83
17
12
88
0
0 gtzj
4 2
8
7
11
2
70
28
21
76
3
gt-l
[1j
5 2
82
16
4
58
38
28
66
6
0 0 1
j6
3
77
20
1
51
48
40
56
4
en
7 5
75
20
5
43
52
45
50
5
~ 0 0
8 6
70
24
8
35
57
44
47
9
~
gt-l
9 5
6
7
28
2
40
58
46
46
8
10
7
68
25
5
40
55
42
50
8
15
7
70
23
2
45
53
46
50
4
20
8
65
27
4
46
50
37
58
5
30
8
74
18
0
57
43
35
63
2
40
7
72
21
3
53
44
29
62
9
50
13
62
25
4
49
47
31
59
10
10
0
15
6
0
25
1
52
47
27
71
2
20
0
17
64
19
2
55
43
26
68
6
30
0
12
74
14
7
63
30
17
74
9
w
(T
able
3 C
ontd
)
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Job
S
co
Ps
CD
S E
QC
GP
SC
DS
SCD
S C
GPS
SC
GP
S N
EH
E
QC
GP
SN
EH
S
NE
H
CG
PS
NE
H
SCD
S E
Q
EH
CD
S SC
DS
NE
Il
N
400
14
63
23
5
58
37
14
81
5
500
13
62
25
1
57
42
20
78
2
60
0
9
61
30
4
54
42
12
82
6
700
11
66
23
8
56
36
13
79
8
80
0
10
63
27
4
57
39
14
82
4
900
16
63
21
2
60
38
17
81
2
1000
11
61
28
4
57
39
12
82
6
Sm
all
pro
ble
ms
Min
Max
Avg
0
67
9
7
91
28
375
00
77
125
0
191
250
0
8
337
50
35
17
83
58
525
000
44
1250
12
46
347
500
46
88
598
750
0
9
537
50
Lar
ge
pro
ble
ms
Min
Max
7
17
60
74
14
30
0
8
45
63
30
53
12
46
50
82
2
10
114
00
65
3333
2
326
67
3
4000
546
000
42
0000
233
333
71
333
3
533
33
Ov
eral
l p
rob
lem
s
Min
Max
Avg
0
60
9
17
91
30
873
91
69
434
8 2
182
61
0
8
339
13
35
17
83
58
538
696
42
739
1
12
46
273
043
~~~--~--
46
0
88
10
673
478
5
3478
c 0 ~ gtshy L
0
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343 ANALYSIS OF THE CGPS ALGORITHM
average-case performance ratio the NEH algorithm is the best one folshylowed by the CDS algorithm and then by the CGPS algorithm As comshypared with the CDS algorithm in terms of the proportional superiority 87391 of problem instances better by the CGPS algorithm 694348 of problem instances equal by the CGPS and CDS algorithms and 218261 of problem instances better by the CDS algorithm on the average It follows that the CDS algorithm is better than the CGPS alshygorithm especially for the large problems Similarly the NEH algorithm is better than the CGPS algorithm and the NEH algorithm is better than the CDS algorithm especially for the small problems Therefore we have the same conclusion about these heuristics in terms of the empirishycal average-case performance ratio and proportional superiority
To sum up 503478 ofthe initial permutation schedules generated by the CGPS algorithm are the optimal schedules Among the 496522 of non-optimal schedules 647154 of the permutation schedules 1t2 improve the makespan of the initial permutation schedule However the improvement made by 1t2 is only 03940 over 1tl in terms of the empirical average-case performance ratio Moreover compared with the NEH and CDS algorithms the CGPS algorithm is the worst one in terms of the empirical average-case performance ratio and proportional superiority These imply that the CGPS algorithm is not good enough for applications
5 CONCLUSION
In this paper the 3FSS with minimum makespan criterion is considered Theoretically the heuristic algorithm proposed by Chen et a1 [2] has the best worst-case performance ratio of 53 This paper evaluates the performance of the CGPS algorithm from the empirical perspective Three simulation experiments are performed to analyze its performance in terms of the six evaluation measures on a total of 13800 problems Four conclusions can be drawn from this paper Firstly in the CGPS algorithm 503478 of the initial permutation schedules are the optimal ones 321326 of the permutation schedshyules 1t2 improve the makespan of nl and 175196 of the permutation schedules n2 do not improve their makespan Secondly the empirical average-case performance ratio of initial permutation schedule genershyated by the CGPS algorithm is always smaller than the performance guarantee 53 developed by Chen et al [2] Its empirical average-case performance ratio is in the range of 10011 and 10355 and is 10156 on the average Thirdly the permutation schedule 1t2 improves 1tl in terms of the empirical average-case performance ratio by the non-significant percentage 03940 Fourthly among the three heuristic algorithms the
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344 H C TANG
NEH algorithm is the best one in terms of both empirical average-case performance ratio and proportional superiority followed by the CDS alshygorithm and then by the CGPS algorithm Consequently the CGPS algoshyrithm is not suitable for the 3FSS with minimum makespan criterion
REFERENCES
l H G Campbell R A Dudek and M L Smith A heuristic algorithm for the n-job tn-machine sequencing problem Management Science Vol 16 (1970) pp 630-637
2 B Chen C A Glass C N Potts and V A Strusevich A new heuristic for threeshymachine flow shop scheduling Operations Research Vol 44 (1996) pp 891-898
3 R W Conway W L Maxwell and L W Miller Theory of Scheduling AddisonshyWesley Reading MA 1967
4 R A Dudek S S Panwalkar and M L Smith The lessons of flowshop scheduling research Operations Research Vol 40 (1992) pp 7-13
5 M R Garey D S Johnson and R Sethi The complexity of flowshop and jobshop scheduling Mathematics Operations Research Vol 1 (1976) pp 117-129
6 S M Johnson Optimal two- and three-stage production schedules with setup times included Naval Research Logistic Quarterly Vol 1 (1954) pp 61-68
7 B J Lageweg J K Lenstra and A H G Rinnooy Kan A general bounding scheme for the permutation flow-shop problem Operations Research Vol 26 (1978) pp 53-67
8 E L Lawler J K Lenstra A H G Rinnooy Kan and D B Shmoys Sequencing and Scheduling Algorithms and Complexity in S C Graves A H G Rinnooy
-Kan and P H Zipkin Handbooks in Operations Research and Management Science Vol 4 Logistics of Production and Inventory North Holland Amsterdam 1993 pp 445-522
9 J K Lenstra A H G Rinnooy Kan and P Brucker Complexity of machine scheduling problems Annals Discrete Mathematics Vol 1 (1977) pp 343-362
10 M Nawaz E E Enscore Jr and I Ham A heuristic algorithm for the m-machine n-job flow-shop sequencing problem OMEGA Vol 11 (1983) pp 91-95
II C N Potts An Adaptive branching rule for the permutation flow-shop problem European Journal of Operational Research Vol 5 (1980) pp 19-25
12 A H G Rinnooy Kan Machine scheduling problems classification complexity and computations Nijhoff The Hague 1976
13 H Rock and G Schmidt Machine aggregation heuristics in shop scheduling Methods of Operations Research Vol 45 (1983) pp 303-314
14 C Smutnicki Some results of the worst-case analysis for flow shop scheduling European Journal of Operational Research Vol 109 (1998) pp 66-87
15 S M A Suliman A two-phase heuristic approach to the permutation flow-shop scheduling problem International Journal of Production Economics Vol 64 (2000) pp 143-152
16 H C Tang A new lower bonding rule for permutation flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 249-257
17 H C Tang A modification to the CGPS algorithm for three-machine flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 321-331
Received January 2001
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Con
diti
ons
~ z
Job
E
i R
i Ii
gt
C
l C
2 C
3 C
4 C
5 C
6 C
7 C
8 S CD
26
40
16
0 1
0 17
0
540
541
0
8209
470
600
CD
0 gt1j
8 (7
) 26
74
0
0 0
0 0
0 10
000
00
0
0000
NA
gt-3 r
(9)
26
74
0 0
0 0
0 0
100
0000
000
00
N
A
ttl
()
36
29
12
0 1
0 22
0
453
125
0
7050
342
900
0 0
9
(7)
36
64
0 0
0 0
0 0
100
0000
000
00
N
A
CD
t
(9)
36
64
0 0
0 0
0 0
100
0000
000
00
N
A
0 ~
0 0(3
) 35
29
19
0
0 0
17
0 44
615
4
079
33
52
780
0
gt-3 r10
(7
) 35
65
0
0 0
0 0
0 10
000
00
0
0000
NA
s
(9
) 35
65
0
0 0
0 0
0 10
000
00
0
0000
NA
(3
) 44
12
20
0
0 0
24
0 21
428
6
054
37
45
450
0
15
(7)
44
56
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
44
56
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
42
9 27
0
0 0
22
0 15
517
2
113
59
55
100
0
20
(7)
42
58
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
42
58
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
53
6 19
0
0 0
22
0 12
766
0
050
21
46
340
0
30
(7)
53
47
0 0
0 0
0 0
100
0000
000
00
N
A
53
47
0 ()
0 0
0 0
100
0000
000
00
N
A
CJ
) C
J)
Of
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w
Job
--~~
~~-
~--
Ri
Ii
w
CIl
Cl
C2
C
3 C
4
C5
C6
C7
C
8 -----------shy
(3)
51
3 21
0
1 0
24
0
61
22
4
0
46
39
456
500
40
(7
) 51
4
9
0 0
0 0
0 0
10
00
00
0
0
00
00
NA
(9
) 51
4
9
0 -----_
_
-shy
0 0
0 --shy-----~~~~
0 0
-----------shy
10
00
00
0
0
00
00
---------shy
NA
(3
) 4
8
4 33
0
0 0
15
0
769
23
0
62
20
68
75
00
50
(7
) 4
8
52
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
48
5
2
0 0
0 0
0 0
100
0000
00
00
0
N
A
51
4 3
7
0 0
0 8
0 8
16
33
05
05
8
8
22
20
0
10
0
(7)
51
49
0
0 0
0 0
0 10
000
00
0
00
00
NA
(9
) 51
4
9
0 0
0 0
0 0
100
0000
00
00
0
N
A
----------shy
(3)
56
0
35
0 0
0 9
0 0
00
00
02
29
1
79
550
0
20
0
(7)
56
4
4
0 0
0 0
0 0
100
0000
00
00
0
N
A
(9)
56
4
4
0 0
0 0
0 0
100
0000
00
00
0
N
A
69
2
24
0
0 0
5 0
64
51
6
0
13
97
82
76
00
30
0
(7)
69
31
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
(9)
69
31
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
(3)
62
1
35
0
0 0
2 0
26
31
6
0
17
96
94
59
00
~
40
0
(7)
62
3
8
0 0
0 0
0 0
10
00
00
0
0
00
00
NA
0
(9)
62
3
8
0 0
0 0
0 0
100
0000
00
00
0
N
A
fja
tJte
2-C
onat
)
gt-3
)shy z Cl
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0gtshy
Con
diti
ons
Job
E
i R
i Ii
C
l C
2 C
3 C
4 C
5 C
6 C
7 C
8 ~ rt
J gt-
lt57
0
38
0 0
0 5
0 0
0000
015
97
88
370
0
rtJ
j
500
(7)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
0 3
(9)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
tr1
tlj
0(3
) 57
1
39
0 0
0 3
0 2
3256
014
97
92
860
0
0 rtJ
60
0
(7)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
~ 57
43
0
0 0
0 0
0 10
000
00
0
0000
NA
0 0
1 34
0
gt-lt
(3)
64
0 0
1 0
277
78
0
1198
971
400
tO
3
700
(7)
64
36
0 0
0 0
0 0
100
0000
000
00
N
A
tr1 ~
(9)
64
36
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
60
2 38
0
0 0
0 0
500
00
0
0999
100
0000
800
(7)
60
40
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
60
40
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
62
0 38
0
0 0
0 0
000
00
0
0999
100
0000
900
(7)
62
38
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
62
38
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
60
0 39
0
0 0
1 0
000
0
010
99
97
500
0
1000
(7
) 60
40
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 60
40
0
0 0
0 0
0 10
000
00
0
0000
NA
w
w
(T
able
2 C
ontd
)
-l
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------
CIJ
Con
diti
ons
CIJ
Job
E
i R
i Ii
O
J
C1
C2
C3
C4
C
5 C
6 C
7
C8
Min
44
615
4
000
00
0
0000
Sm
all
(3
) M
ax
951
220
0
8209
562
500
pro
ble
ms
Avg
66
121
6
050
01
39
021
3
_
Min
0
0000
009
99
45
450
0
Lar
ge
(3
) M
ax
214
286
1
1359
100
0000
p
rob
lem
s A
vg
605
84
0
3374
784
187
--
-_
__
---
Min
0
0000
000
00
0
0000
Ov
eral
l
(3)
Max
95
122
0
113
59
10
000
00
pro
ble
ms
Avg
26
950
0
039
40
64
715
2
tr1
()
gt-3 ~ Q
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gtshyT
able
3
z C
om
par
ativ
e re
sult
s fo
r th
e C
GP
S
CD
S
an
d N
EH
alg
ori
thm
s ~
Job 3
4
5
6
7
8
9
10
15
20
30
40
50
100
200
101
39
101
48
102
64
102
82
102
62
102
7
102
82
102
55
102
43
101
83
101
06
100
85
100
65
100
32
100
17
~CDS
100
96
101
28
102
14
102
32
102
46
102
15
102
04
102
04
101
78
101
53
100
99
100
63
100
55
100
35
100
17
~~--------------
~NElI
100
1
100
39
100
53
100
54
100
48
100
72
100
50
100
47
100
25
100
33
100
08
100
05
100
06
100
02
100
01
(Tab
le 3
Con
td)
lfl
gt-
lfl
0 tJ r t=J
0 0 i
lfl gtshy r 0 0 ~ r $
w
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-----
---
co
jgt
~CGPS
~CDS
~NE
H 0
300
100
07
100
11
100
00
400
100
04
100
06
100
00
500
100
02
100
06
100
00
600
100
02
100
03
100
00
700
100
01
100
02
100
00
800
100
02
100
02
100
00
900
100
01
100
04
100
00
1000
1
0001
1
0003
1
0000
Min
1
0139
1
0096
1
0010
Sm
all
pro
ble
ms
Max
1
0282
1
0246
1
0072
Avg
1
0238
1
0192
1
0047
Min
1
0001
1
0002
1
0000
Lar
ge
pro
ble
ms
Max
1
0243
1
0178
1
0033
Avg
1
0050
1
0042
1
0005
Min
1
0001
1
0002
1
0000
r
Ov
eral
l p
rob
lem
s M
ax
102
82
102
46
100
72
0 -3A
vg
101
15
100
95
100
20
p Z
(Tab
le 3
Con
td)
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lgtT
able
3 (
Con
td)
Z
--------
_shy
SCG
PS
EQ
CG
PS
SCD
S SC
GP
S E
QC
GP
S SN
EH
SN
EH
EQ
IjE
H
SCD
S --
~ Jo
b C
DS
CDS
CG
PS
NEH
N
EH
CG
PS
CD
S CD
S N
EH
en
en
3 0
91
9
0
83
17
12
88
0
0 gtzj
4 2
8
7
11
2
70
28
21
76
3
gt-l
[1j
5 2
82
16
4
58
38
28
66
6
0 0 1
j6
3
77
20
1
51
48
40
56
4
en
7 5
75
20
5
43
52
45
50
5
~ 0 0
8 6
70
24
8
35
57
44
47
9
~
gt-l
9 5
6
7
28
2
40
58
46
46
8
10
7
68
25
5
40
55
42
50
8
15
7
70
23
2
45
53
46
50
4
20
8
65
27
4
46
50
37
58
5
30
8
74
18
0
57
43
35
63
2
40
7
72
21
3
53
44
29
62
9
50
13
62
25
4
49
47
31
59
10
10
0
15
6
0
25
1
52
47
27
71
2
20
0
17
64
19
2
55
43
26
68
6
30
0
12
74
14
7
63
30
17
74
9
w
(T
able
3 C
ontd
)
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Job
S
co
Ps
CD
S E
QC
GP
SC
DS
SCD
S C
GPS
SC
GP
S N
EH
E
QC
GP
SN
EH
S
NE
H
CG
PS
NE
H
SCD
S E
Q
EH
CD
S SC
DS
NE
Il
N
400
14
63
23
5
58
37
14
81
5
500
13
62
25
1
57
42
20
78
2
60
0
9
61
30
4
54
42
12
82
6
700
11
66
23
8
56
36
13
79
8
80
0
10
63
27
4
57
39
14
82
4
900
16
63
21
2
60
38
17
81
2
1000
11
61
28
4
57
39
12
82
6
Sm
all
pro
ble
ms
Min
Max
Avg
0
67
9
7
91
28
375
00
77
125
0
191
250
0
8
337
50
35
17
83
58
525
000
44
1250
12
46
347
500
46
88
598
750
0
9
537
50
Lar
ge
pro
ble
ms
Min
Max
7
17
60
74
14
30
0
8
45
63
30
53
12
46
50
82
2
10
114
00
65
3333
2
326
67
3
4000
546
000
42
0000
233
333
71
333
3
533
33
Ov
eral
l p
rob
lem
s
Min
Max
Avg
0
60
9
17
91
30
873
91
69
434
8 2
182
61
0
8
339
13
35
17
83
58
538
696
42
739
1
12
46
273
043
~~~--~--
46
0
88
10
673
478
5
3478
c 0 ~ gtshy L
0
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343 ANALYSIS OF THE CGPS ALGORITHM
average-case performance ratio the NEH algorithm is the best one folshylowed by the CDS algorithm and then by the CGPS algorithm As comshypared with the CDS algorithm in terms of the proportional superiority 87391 of problem instances better by the CGPS algorithm 694348 of problem instances equal by the CGPS and CDS algorithms and 218261 of problem instances better by the CDS algorithm on the average It follows that the CDS algorithm is better than the CGPS alshygorithm especially for the large problems Similarly the NEH algorithm is better than the CGPS algorithm and the NEH algorithm is better than the CDS algorithm especially for the small problems Therefore we have the same conclusion about these heuristics in terms of the empirishycal average-case performance ratio and proportional superiority
To sum up 503478 ofthe initial permutation schedules generated by the CGPS algorithm are the optimal schedules Among the 496522 of non-optimal schedules 647154 of the permutation schedules 1t2 improve the makespan of the initial permutation schedule However the improvement made by 1t2 is only 03940 over 1tl in terms of the empirical average-case performance ratio Moreover compared with the NEH and CDS algorithms the CGPS algorithm is the worst one in terms of the empirical average-case performance ratio and proportional superiority These imply that the CGPS algorithm is not good enough for applications
5 CONCLUSION
In this paper the 3FSS with minimum makespan criterion is considered Theoretically the heuristic algorithm proposed by Chen et a1 [2] has the best worst-case performance ratio of 53 This paper evaluates the performance of the CGPS algorithm from the empirical perspective Three simulation experiments are performed to analyze its performance in terms of the six evaluation measures on a total of 13800 problems Four conclusions can be drawn from this paper Firstly in the CGPS algorithm 503478 of the initial permutation schedules are the optimal ones 321326 of the permutation schedshyules 1t2 improve the makespan of nl and 175196 of the permutation schedules n2 do not improve their makespan Secondly the empirical average-case performance ratio of initial permutation schedule genershyated by the CGPS algorithm is always smaller than the performance guarantee 53 developed by Chen et al [2] Its empirical average-case performance ratio is in the range of 10011 and 10355 and is 10156 on the average Thirdly the permutation schedule 1t2 improves 1tl in terms of the empirical average-case performance ratio by the non-significant percentage 03940 Fourthly among the three heuristic algorithms the
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344 H C TANG
NEH algorithm is the best one in terms of both empirical average-case performance ratio and proportional superiority followed by the CDS alshygorithm and then by the CGPS algorithm Consequently the CGPS algoshyrithm is not suitable for the 3FSS with minimum makespan criterion
REFERENCES
l H G Campbell R A Dudek and M L Smith A heuristic algorithm for the n-job tn-machine sequencing problem Management Science Vol 16 (1970) pp 630-637
2 B Chen C A Glass C N Potts and V A Strusevich A new heuristic for threeshymachine flow shop scheduling Operations Research Vol 44 (1996) pp 891-898
3 R W Conway W L Maxwell and L W Miller Theory of Scheduling AddisonshyWesley Reading MA 1967
4 R A Dudek S S Panwalkar and M L Smith The lessons of flowshop scheduling research Operations Research Vol 40 (1992) pp 7-13
5 M R Garey D S Johnson and R Sethi The complexity of flowshop and jobshop scheduling Mathematics Operations Research Vol 1 (1976) pp 117-129
6 S M Johnson Optimal two- and three-stage production schedules with setup times included Naval Research Logistic Quarterly Vol 1 (1954) pp 61-68
7 B J Lageweg J K Lenstra and A H G Rinnooy Kan A general bounding scheme for the permutation flow-shop problem Operations Research Vol 26 (1978) pp 53-67
8 E L Lawler J K Lenstra A H G Rinnooy Kan and D B Shmoys Sequencing and Scheduling Algorithms and Complexity in S C Graves A H G Rinnooy
-Kan and P H Zipkin Handbooks in Operations Research and Management Science Vol 4 Logistics of Production and Inventory North Holland Amsterdam 1993 pp 445-522
9 J K Lenstra A H G Rinnooy Kan and P Brucker Complexity of machine scheduling problems Annals Discrete Mathematics Vol 1 (1977) pp 343-362
10 M Nawaz E E Enscore Jr and I Ham A heuristic algorithm for the m-machine n-job flow-shop sequencing problem OMEGA Vol 11 (1983) pp 91-95
II C N Potts An Adaptive branching rule for the permutation flow-shop problem European Journal of Operational Research Vol 5 (1980) pp 19-25
12 A H G Rinnooy Kan Machine scheduling problems classification complexity and computations Nijhoff The Hague 1976
13 H Rock and G Schmidt Machine aggregation heuristics in shop scheduling Methods of Operations Research Vol 45 (1983) pp 303-314
14 C Smutnicki Some results of the worst-case analysis for flow shop scheduling European Journal of Operational Research Vol 109 (1998) pp 66-87
15 S M A Suliman A two-phase heuristic approach to the permutation flow-shop scheduling problem International Journal of Production Economics Vol 64 (2000) pp 143-152
16 H C Tang A new lower bonding rule for permutation flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 249-257
17 H C Tang A modification to the CGPS algorithm for three-machine flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 321-331
Received January 2001
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w
Job
--~~
~~-
~--
Ri
Ii
w
CIl
Cl
C2
C
3 C
4
C5
C6
C7
C
8 -----------shy
(3)
51
3 21
0
1 0
24
0
61
22
4
0
46
39
456
500
40
(7
) 51
4
9
0 0
0 0
0 0
10
00
00
0
0
00
00
NA
(9
) 51
4
9
0 -----_
_
-shy
0 0
0 --shy-----~~~~
0 0
-----------shy
10
00
00
0
0
00
00
---------shy
NA
(3
) 4
8
4 33
0
0 0
15
0
769
23
0
62
20
68
75
00
50
(7
) 4
8
52
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
48
5
2
0 0
0 0
0 0
100
0000
00
00
0
N
A
51
4 3
7
0 0
0 8
0 8
16
33
05
05
8
8
22
20
0
10
0
(7)
51
49
0
0 0
0 0
0 10
000
00
0
00
00
NA
(9
) 51
4
9
0 0
0 0
0 0
100
0000
00
00
0
N
A
----------shy
(3)
56
0
35
0 0
0 9
0 0
00
00
02
29
1
79
550
0
20
0
(7)
56
4
4
0 0
0 0
0 0
100
0000
00
00
0
N
A
(9)
56
4
4
0 0
0 0
0 0
100
0000
00
00
0
N
A
69
2
24
0
0 0
5 0
64
51
6
0
13
97
82
76
00
30
0
(7)
69
31
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
(9)
69
31
0
0 0
0 0
0 1
00
00
00
00
00
0
N
A
(3)
62
1
35
0
0 0
2 0
26
31
6
0
17
96
94
59
00
~
40
0
(7)
62
3
8
0 0
0 0
0 0
10
00
00
0
0
00
00
NA
0
(9)
62
3
8
0 0
0 0
0 0
100
0000
00
00
0
N
A
fja
tJte
2-C
onat
)
gt-3
)shy z Cl
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0gtshy
Con
diti
ons
Job
E
i R
i Ii
C
l C
2 C
3 C
4 C
5 C
6 C
7 C
8 ~ rt
J gt-
lt57
0
38
0 0
0 5
0 0
0000
015
97
88
370
0
rtJ
j
500
(7)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
0 3
(9)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
tr1
tlj
0(3
) 57
1
39
0 0
0 3
0 2
3256
014
97
92
860
0
0 rtJ
60
0
(7)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
~ 57
43
0
0 0
0 0
0 10
000
00
0
0000
NA
0 0
1 34
0
gt-lt
(3)
64
0 0
1 0
277
78
0
1198
971
400
tO
3
700
(7)
64
36
0 0
0 0
0 0
100
0000
000
00
N
A
tr1 ~
(9)
64
36
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
60
2 38
0
0 0
0 0
500
00
0
0999
100
0000
800
(7)
60
40
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
60
40
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
62
0 38
0
0 0
0 0
000
00
0
0999
100
0000
900
(7)
62
38
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
62
38
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
60
0 39
0
0 0
1 0
000
0
010
99
97
500
0
1000
(7
) 60
40
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 60
40
0
0 0
0 0
0 10
000
00
0
0000
NA
w
w
(T
able
2 C
ontd
)
-l
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------
CIJ
Con
diti
ons
CIJ
Job
E
i R
i Ii
O
J
C1
C2
C3
C4
C
5 C
6 C
7
C8
Min
44
615
4
000
00
0
0000
Sm
all
(3
) M
ax
951
220
0
8209
562
500
pro
ble
ms
Avg
66
121
6
050
01
39
021
3
_
Min
0
0000
009
99
45
450
0
Lar
ge
(3
) M
ax
214
286
1
1359
100
0000
p
rob
lem
s A
vg
605
84
0
3374
784
187
--
-_
__
---
Min
0
0000
000
00
0
0000
Ov
eral
l
(3)
Max
95
122
0
113
59
10
000
00
pro
ble
ms
Avg
26
950
0
039
40
64
715
2
tr1
()
gt-3 ~ Q
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gtshyT
able
3
z C
om
par
ativ
e re
sult
s fo
r th
e C
GP
S
CD
S
an
d N
EH
alg
ori
thm
s ~
Job 3
4
5
6
7
8
9
10
15
20
30
40
50
100
200
101
39
101
48
102
64
102
82
102
62
102
7
102
82
102
55
102
43
101
83
101
06
100
85
100
65
100
32
100
17
~CDS
100
96
101
28
102
14
102
32
102
46
102
15
102
04
102
04
101
78
101
53
100
99
100
63
100
55
100
35
100
17
~~--------------
~NElI
100
1
100
39
100
53
100
54
100
48
100
72
100
50
100
47
100
25
100
33
100
08
100
05
100
06
100
02
100
01
(Tab
le 3
Con
td)
lfl
gt-
lfl
0 tJ r t=J
0 0 i
lfl gtshy r 0 0 ~ r $
w
w
to
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-----
---
co
jgt
~CGPS
~CDS
~NE
H 0
300
100
07
100
11
100
00
400
100
04
100
06
100
00
500
100
02
100
06
100
00
600
100
02
100
03
100
00
700
100
01
100
02
100
00
800
100
02
100
02
100
00
900
100
01
100
04
100
00
1000
1
0001
1
0003
1
0000
Min
1
0139
1
0096
1
0010
Sm
all
pro
ble
ms
Max
1
0282
1
0246
1
0072
Avg
1
0238
1
0192
1
0047
Min
1
0001
1
0002
1
0000
Lar
ge
pro
ble
ms
Max
1
0243
1
0178
1
0033
Avg
1
0050
1
0042
1
0005
Min
1
0001
1
0002
1
0000
r
Ov
eral
l p
rob
lem
s M
ax
102
82
102
46
100
72
0 -3A
vg
101
15
100
95
100
20
p Z
(Tab
le 3
Con
td)
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lgtT
able
3 (
Con
td)
Z
--------
_shy
SCG
PS
EQ
CG
PS
SCD
S SC
GP
S E
QC
GP
S SN
EH
SN
EH
EQ
IjE
H
SCD
S --
~ Jo
b C
DS
CDS
CG
PS
NEH
N
EH
CG
PS
CD
S CD
S N
EH
en
en
3 0
91
9
0
83
17
12
88
0
0 gtzj
4 2
8
7
11
2
70
28
21
76
3
gt-l
[1j
5 2
82
16
4
58
38
28
66
6
0 0 1
j6
3
77
20
1
51
48
40
56
4
en
7 5
75
20
5
43
52
45
50
5
~ 0 0
8 6
70
24
8
35
57
44
47
9
~
gt-l
9 5
6
7
28
2
40
58
46
46
8
10
7
68
25
5
40
55
42
50
8
15
7
70
23
2
45
53
46
50
4
20
8
65
27
4
46
50
37
58
5
30
8
74
18
0
57
43
35
63
2
40
7
72
21
3
53
44
29
62
9
50
13
62
25
4
49
47
31
59
10
10
0
15
6
0
25
1
52
47
27
71
2
20
0
17
64
19
2
55
43
26
68
6
30
0
12
74
14
7
63
30
17
74
9
w
(T
able
3 C
ontd
)
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Job
S
co
Ps
CD
S E
QC
GP
SC
DS
SCD
S C
GPS
SC
GP
S N
EH
E
QC
GP
SN
EH
S
NE
H
CG
PS
NE
H
SCD
S E
Q
EH
CD
S SC
DS
NE
Il
N
400
14
63
23
5
58
37
14
81
5
500
13
62
25
1
57
42
20
78
2
60
0
9
61
30
4
54
42
12
82
6
700
11
66
23
8
56
36
13
79
8
80
0
10
63
27
4
57
39
14
82
4
900
16
63
21
2
60
38
17
81
2
1000
11
61
28
4
57
39
12
82
6
Sm
all
pro
ble
ms
Min
Max
Avg
0
67
9
7
91
28
375
00
77
125
0
191
250
0
8
337
50
35
17
83
58
525
000
44
1250
12
46
347
500
46
88
598
750
0
9
537
50
Lar
ge
pro
ble
ms
Min
Max
7
17
60
74
14
30
0
8
45
63
30
53
12
46
50
82
2
10
114
00
65
3333
2
326
67
3
4000
546
000
42
0000
233
333
71
333
3
533
33
Ov
eral
l p
rob
lem
s
Min
Max
Avg
0
60
9
17
91
30
873
91
69
434
8 2
182
61
0
8
339
13
35
17
83
58
538
696
42
739
1
12
46
273
043
~~~--~--
46
0
88
10
673
478
5
3478
c 0 ~ gtshy L
0
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343 ANALYSIS OF THE CGPS ALGORITHM
average-case performance ratio the NEH algorithm is the best one folshylowed by the CDS algorithm and then by the CGPS algorithm As comshypared with the CDS algorithm in terms of the proportional superiority 87391 of problem instances better by the CGPS algorithm 694348 of problem instances equal by the CGPS and CDS algorithms and 218261 of problem instances better by the CDS algorithm on the average It follows that the CDS algorithm is better than the CGPS alshygorithm especially for the large problems Similarly the NEH algorithm is better than the CGPS algorithm and the NEH algorithm is better than the CDS algorithm especially for the small problems Therefore we have the same conclusion about these heuristics in terms of the empirishycal average-case performance ratio and proportional superiority
To sum up 503478 ofthe initial permutation schedules generated by the CGPS algorithm are the optimal schedules Among the 496522 of non-optimal schedules 647154 of the permutation schedules 1t2 improve the makespan of the initial permutation schedule However the improvement made by 1t2 is only 03940 over 1tl in terms of the empirical average-case performance ratio Moreover compared with the NEH and CDS algorithms the CGPS algorithm is the worst one in terms of the empirical average-case performance ratio and proportional superiority These imply that the CGPS algorithm is not good enough for applications
5 CONCLUSION
In this paper the 3FSS with minimum makespan criterion is considered Theoretically the heuristic algorithm proposed by Chen et a1 [2] has the best worst-case performance ratio of 53 This paper evaluates the performance of the CGPS algorithm from the empirical perspective Three simulation experiments are performed to analyze its performance in terms of the six evaluation measures on a total of 13800 problems Four conclusions can be drawn from this paper Firstly in the CGPS algorithm 503478 of the initial permutation schedules are the optimal ones 321326 of the permutation schedshyules 1t2 improve the makespan of nl and 175196 of the permutation schedules n2 do not improve their makespan Secondly the empirical average-case performance ratio of initial permutation schedule genershyated by the CGPS algorithm is always smaller than the performance guarantee 53 developed by Chen et al [2] Its empirical average-case performance ratio is in the range of 10011 and 10355 and is 10156 on the average Thirdly the permutation schedule 1t2 improves 1tl in terms of the empirical average-case performance ratio by the non-significant percentage 03940 Fourthly among the three heuristic algorithms the
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344 H C TANG
NEH algorithm is the best one in terms of both empirical average-case performance ratio and proportional superiority followed by the CDS alshygorithm and then by the CGPS algorithm Consequently the CGPS algoshyrithm is not suitable for the 3FSS with minimum makespan criterion
REFERENCES
l H G Campbell R A Dudek and M L Smith A heuristic algorithm for the n-job tn-machine sequencing problem Management Science Vol 16 (1970) pp 630-637
2 B Chen C A Glass C N Potts and V A Strusevich A new heuristic for threeshymachine flow shop scheduling Operations Research Vol 44 (1996) pp 891-898
3 R W Conway W L Maxwell and L W Miller Theory of Scheduling AddisonshyWesley Reading MA 1967
4 R A Dudek S S Panwalkar and M L Smith The lessons of flowshop scheduling research Operations Research Vol 40 (1992) pp 7-13
5 M R Garey D S Johnson and R Sethi The complexity of flowshop and jobshop scheduling Mathematics Operations Research Vol 1 (1976) pp 117-129
6 S M Johnson Optimal two- and three-stage production schedules with setup times included Naval Research Logistic Quarterly Vol 1 (1954) pp 61-68
7 B J Lageweg J K Lenstra and A H G Rinnooy Kan A general bounding scheme for the permutation flow-shop problem Operations Research Vol 26 (1978) pp 53-67
8 E L Lawler J K Lenstra A H G Rinnooy Kan and D B Shmoys Sequencing and Scheduling Algorithms and Complexity in S C Graves A H G Rinnooy
-Kan and P H Zipkin Handbooks in Operations Research and Management Science Vol 4 Logistics of Production and Inventory North Holland Amsterdam 1993 pp 445-522
9 J K Lenstra A H G Rinnooy Kan and P Brucker Complexity of machine scheduling problems Annals Discrete Mathematics Vol 1 (1977) pp 343-362
10 M Nawaz E E Enscore Jr and I Ham A heuristic algorithm for the m-machine n-job flow-shop sequencing problem OMEGA Vol 11 (1983) pp 91-95
II C N Potts An Adaptive branching rule for the permutation flow-shop problem European Journal of Operational Research Vol 5 (1980) pp 19-25
12 A H G Rinnooy Kan Machine scheduling problems classification complexity and computations Nijhoff The Hague 1976
13 H Rock and G Schmidt Machine aggregation heuristics in shop scheduling Methods of Operations Research Vol 45 (1983) pp 303-314
14 C Smutnicki Some results of the worst-case analysis for flow shop scheduling European Journal of Operational Research Vol 109 (1998) pp 66-87
15 S M A Suliman A two-phase heuristic approach to the permutation flow-shop scheduling problem International Journal of Production Economics Vol 64 (2000) pp 143-152
16 H C Tang A new lower bonding rule for permutation flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 249-257
17 H C Tang A modification to the CGPS algorithm for three-machine flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 321-331
Received January 2001
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0gtshy
Con
diti
ons
Job
E
i R
i Ii
C
l C
2 C
3 C
4 C
5 C
6 C
7 C
8 ~ rt
J gt-
lt57
0
38
0 0
0 5
0 0
0000
015
97
88
370
0
rtJ
j
500
(7)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
0 3
(9)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
tr1
tlj
0(3
) 57
1
39
0 0
0 3
0 2
3256
014
97
92
860
0
0 rtJ
60
0
(7)
57
43
0 0
0 0
0 0
100
0000
000
00
N
A
~ 57
43
0
0 0
0 0
0 10
000
00
0
0000
NA
0 0
1 34
0
gt-lt
(3)
64
0 0
1 0
277
78
0
1198
971
400
tO
3
700
(7)
64
36
0 0
0 0
0 0
100
0000
000
00
N
A
tr1 ~
(9)
64
36
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
60
2 38
0
0 0
0 0
500
00
0
0999
100
0000
800
(7)
60
40
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
60
40
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
62
0 38
0
0 0
0 0
000
00
0
0999
100
0000
900
(7)
62
38
0 0
0 0
0 0
100
0000
000
00
N
A
(9)
62
38
0 0
0 0
0 0
100
0000
000
00
N
A
(3)
60
0 39
0
0 0
1 0
000
0
010
99
97
500
0
1000
(7
) 60
40
0
0 0
0 0
0 10
000
00
0
0000
NA
(9
) 60
40
0
0 0
0 0
0 10
000
00
0
0000
NA
w
w
(T
able
2 C
ontd
)
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------
CIJ
Con
diti
ons
CIJ
Job
E
i R
i Ii
O
J
C1
C2
C3
C4
C
5 C
6 C
7
C8
Min
44
615
4
000
00
0
0000
Sm
all
(3
) M
ax
951
220
0
8209
562
500
pro
ble
ms
Avg
66
121
6
050
01
39
021
3
_
Min
0
0000
009
99
45
450
0
Lar
ge
(3
) M
ax
214
286
1
1359
100
0000
p
rob
lem
s A
vg
605
84
0
3374
784
187
--
-_
__
---
Min
0
0000
000
00
0
0000
Ov
eral
l
(3)
Max
95
122
0
113
59
10
000
00
pro
ble
ms
Avg
26
950
0
039
40
64
715
2
tr1
()
gt-3 ~ Q
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gtshyT
able
3
z C
om
par
ativ
e re
sult
s fo
r th
e C
GP
S
CD
S
an
d N
EH
alg
ori
thm
s ~
Job 3
4
5
6
7
8
9
10
15
20
30
40
50
100
200
101
39
101
48
102
64
102
82
102
62
102
7
102
82
102
55
102
43
101
83
101
06
100
85
100
65
100
32
100
17
~CDS
100
96
101
28
102
14
102
32
102
46
102
15
102
04
102
04
101
78
101
53
100
99
100
63
100
55
100
35
100
17
~~--------------
~NElI
100
1
100
39
100
53
100
54
100
48
100
72
100
50
100
47
100
25
100
33
100
08
100
05
100
06
100
02
100
01
(Tab
le 3
Con
td)
lfl
gt-
lfl
0 tJ r t=J
0 0 i
lfl gtshy r 0 0 ~ r $
w
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-----
---
co
jgt
~CGPS
~CDS
~NE
H 0
300
100
07
100
11
100
00
400
100
04
100
06
100
00
500
100
02
100
06
100
00
600
100
02
100
03
100
00
700
100
01
100
02
100
00
800
100
02
100
02
100
00
900
100
01
100
04
100
00
1000
1
0001
1
0003
1
0000
Min
1
0139
1
0096
1
0010
Sm
all
pro
ble
ms
Max
1
0282
1
0246
1
0072
Avg
1
0238
1
0192
1
0047
Min
1
0001
1
0002
1
0000
Lar
ge
pro
ble
ms
Max
1
0243
1
0178
1
0033
Avg
1
0050
1
0042
1
0005
Min
1
0001
1
0002
1
0000
r
Ov
eral
l p
rob
lem
s M
ax
102
82
102
46
100
72
0 -3A
vg
101
15
100
95
100
20
p Z
(Tab
le 3
Con
td)
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lgtT
able
3 (
Con
td)
Z
--------
_shy
SCG
PS
EQ
CG
PS
SCD
S SC
GP
S E
QC
GP
S SN
EH
SN
EH
EQ
IjE
H
SCD
S --
~ Jo
b C
DS
CDS
CG
PS
NEH
N
EH
CG
PS
CD
S CD
S N
EH
en
en
3 0
91
9
0
83
17
12
88
0
0 gtzj
4 2
8
7
11
2
70
28
21
76
3
gt-l
[1j
5 2
82
16
4
58
38
28
66
6
0 0 1
j6
3
77
20
1
51
48
40
56
4
en
7 5
75
20
5
43
52
45
50
5
~ 0 0
8 6
70
24
8
35
57
44
47
9
~
gt-l
9 5
6
7
28
2
40
58
46
46
8
10
7
68
25
5
40
55
42
50
8
15
7
70
23
2
45
53
46
50
4
20
8
65
27
4
46
50
37
58
5
30
8
74
18
0
57
43
35
63
2
40
7
72
21
3
53
44
29
62
9
50
13
62
25
4
49
47
31
59
10
10
0
15
6
0
25
1
52
47
27
71
2
20
0
17
64
19
2
55
43
26
68
6
30
0
12
74
14
7
63
30
17
74
9
w
(T
able
3 C
ontd
)
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Job
S
co
Ps
CD
S E
QC
GP
SC
DS
SCD
S C
GPS
SC
GP
S N
EH
E
QC
GP
SN
EH
S
NE
H
CG
PS
NE
H
SCD
S E
Q
EH
CD
S SC
DS
NE
Il
N
400
14
63
23
5
58
37
14
81
5
500
13
62
25
1
57
42
20
78
2
60
0
9
61
30
4
54
42
12
82
6
700
11
66
23
8
56
36
13
79
8
80
0
10
63
27
4
57
39
14
82
4
900
16
63
21
2
60
38
17
81
2
1000
11
61
28
4
57
39
12
82
6
Sm
all
pro
ble
ms
Min
Max
Avg
0
67
9
7
91
28
375
00
77
125
0
191
250
0
8
337
50
35
17
83
58
525
000
44
1250
12
46
347
500
46
88
598
750
0
9
537
50
Lar
ge
pro
ble
ms
Min
Max
7
17
60
74
14
30
0
8
45
63
30
53
12
46
50
82
2
10
114
00
65
3333
2
326
67
3
4000
546
000
42
0000
233
333
71
333
3
533
33
Ov
eral
l p
rob
lem
s
Min
Max
Avg
0
60
9
17
91
30
873
91
69
434
8 2
182
61
0
8
339
13
35
17
83
58
538
696
42
739
1
12
46
273
043
~~~--~--
46
0
88
10
673
478
5
3478
c 0 ~ gtshy L
0
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343 ANALYSIS OF THE CGPS ALGORITHM
average-case performance ratio the NEH algorithm is the best one folshylowed by the CDS algorithm and then by the CGPS algorithm As comshypared with the CDS algorithm in terms of the proportional superiority 87391 of problem instances better by the CGPS algorithm 694348 of problem instances equal by the CGPS and CDS algorithms and 218261 of problem instances better by the CDS algorithm on the average It follows that the CDS algorithm is better than the CGPS alshygorithm especially for the large problems Similarly the NEH algorithm is better than the CGPS algorithm and the NEH algorithm is better than the CDS algorithm especially for the small problems Therefore we have the same conclusion about these heuristics in terms of the empirishycal average-case performance ratio and proportional superiority
To sum up 503478 ofthe initial permutation schedules generated by the CGPS algorithm are the optimal schedules Among the 496522 of non-optimal schedules 647154 of the permutation schedules 1t2 improve the makespan of the initial permutation schedule However the improvement made by 1t2 is only 03940 over 1tl in terms of the empirical average-case performance ratio Moreover compared with the NEH and CDS algorithms the CGPS algorithm is the worst one in terms of the empirical average-case performance ratio and proportional superiority These imply that the CGPS algorithm is not good enough for applications
5 CONCLUSION
In this paper the 3FSS with minimum makespan criterion is considered Theoretically the heuristic algorithm proposed by Chen et a1 [2] has the best worst-case performance ratio of 53 This paper evaluates the performance of the CGPS algorithm from the empirical perspective Three simulation experiments are performed to analyze its performance in terms of the six evaluation measures on a total of 13800 problems Four conclusions can be drawn from this paper Firstly in the CGPS algorithm 503478 of the initial permutation schedules are the optimal ones 321326 of the permutation schedshyules 1t2 improve the makespan of nl and 175196 of the permutation schedules n2 do not improve their makespan Secondly the empirical average-case performance ratio of initial permutation schedule genershyated by the CGPS algorithm is always smaller than the performance guarantee 53 developed by Chen et al [2] Its empirical average-case performance ratio is in the range of 10011 and 10355 and is 10156 on the average Thirdly the permutation schedule 1t2 improves 1tl in terms of the empirical average-case performance ratio by the non-significant percentage 03940 Fourthly among the three heuristic algorithms the
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344 H C TANG
NEH algorithm is the best one in terms of both empirical average-case performance ratio and proportional superiority followed by the CDS alshygorithm and then by the CGPS algorithm Consequently the CGPS algoshyrithm is not suitable for the 3FSS with minimum makespan criterion
REFERENCES
l H G Campbell R A Dudek and M L Smith A heuristic algorithm for the n-job tn-machine sequencing problem Management Science Vol 16 (1970) pp 630-637
2 B Chen C A Glass C N Potts and V A Strusevich A new heuristic for threeshymachine flow shop scheduling Operations Research Vol 44 (1996) pp 891-898
3 R W Conway W L Maxwell and L W Miller Theory of Scheduling AddisonshyWesley Reading MA 1967
4 R A Dudek S S Panwalkar and M L Smith The lessons of flowshop scheduling research Operations Research Vol 40 (1992) pp 7-13
5 M R Garey D S Johnson and R Sethi The complexity of flowshop and jobshop scheduling Mathematics Operations Research Vol 1 (1976) pp 117-129
6 S M Johnson Optimal two- and three-stage production schedules with setup times included Naval Research Logistic Quarterly Vol 1 (1954) pp 61-68
7 B J Lageweg J K Lenstra and A H G Rinnooy Kan A general bounding scheme for the permutation flow-shop problem Operations Research Vol 26 (1978) pp 53-67
8 E L Lawler J K Lenstra A H G Rinnooy Kan and D B Shmoys Sequencing and Scheduling Algorithms and Complexity in S C Graves A H G Rinnooy
-Kan and P H Zipkin Handbooks in Operations Research and Management Science Vol 4 Logistics of Production and Inventory North Holland Amsterdam 1993 pp 445-522
9 J K Lenstra A H G Rinnooy Kan and P Brucker Complexity of machine scheduling problems Annals Discrete Mathematics Vol 1 (1977) pp 343-362
10 M Nawaz E E Enscore Jr and I Ham A heuristic algorithm for the m-machine n-job flow-shop sequencing problem OMEGA Vol 11 (1983) pp 91-95
II C N Potts An Adaptive branching rule for the permutation flow-shop problem European Journal of Operational Research Vol 5 (1980) pp 19-25
12 A H G Rinnooy Kan Machine scheduling problems classification complexity and computations Nijhoff The Hague 1976
13 H Rock and G Schmidt Machine aggregation heuristics in shop scheduling Methods of Operations Research Vol 45 (1983) pp 303-314
14 C Smutnicki Some results of the worst-case analysis for flow shop scheduling European Journal of Operational Research Vol 109 (1998) pp 66-87
15 S M A Suliman A two-phase heuristic approach to the permutation flow-shop scheduling problem International Journal of Production Economics Vol 64 (2000) pp 143-152
16 H C Tang A new lower bonding rule for permutation flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 249-257
17 H C Tang A modification to the CGPS algorithm for three-machine flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 321-331
Received January 2001
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------
CIJ
Con
diti
ons
CIJ
Job
E
i R
i Ii
O
J
C1
C2
C3
C4
C
5 C
6 C
7
C8
Min
44
615
4
000
00
0
0000
Sm
all
(3
) M
ax
951
220
0
8209
562
500
pro
ble
ms
Avg
66
121
6
050
01
39
021
3
_
Min
0
0000
009
99
45
450
0
Lar
ge
(3
) M
ax
214
286
1
1359
100
0000
p
rob
lem
s A
vg
605
84
0
3374
784
187
--
-_
__
---
Min
0
0000
000
00
0
0000
Ov
eral
l
(3)
Max
95
122
0
113
59
10
000
00
pro
ble
ms
Avg
26
950
0
039
40
64
715
2
tr1
()
gt-3 ~ Q
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gtshyT
able
3
z C
om
par
ativ
e re
sult
s fo
r th
e C
GP
S
CD
S
an
d N
EH
alg
ori
thm
s ~
Job 3
4
5
6
7
8
9
10
15
20
30
40
50
100
200
101
39
101
48
102
64
102
82
102
62
102
7
102
82
102
55
102
43
101
83
101
06
100
85
100
65
100
32
100
17
~CDS
100
96
101
28
102
14
102
32
102
46
102
15
102
04
102
04
101
78
101
53
100
99
100
63
100
55
100
35
100
17
~~--------------
~NElI
100
1
100
39
100
53
100
54
100
48
100
72
100
50
100
47
100
25
100
33
100
08
100
05
100
06
100
02
100
01
(Tab
le 3
Con
td)
lfl
gt-
lfl
0 tJ r t=J
0 0 i
lfl gtshy r 0 0 ~ r $
w
w
to
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-----
---
co
jgt
~CGPS
~CDS
~NE
H 0
300
100
07
100
11
100
00
400
100
04
100
06
100
00
500
100
02
100
06
100
00
600
100
02
100
03
100
00
700
100
01
100
02
100
00
800
100
02
100
02
100
00
900
100
01
100
04
100
00
1000
1
0001
1
0003
1
0000
Min
1
0139
1
0096
1
0010
Sm
all
pro
ble
ms
Max
1
0282
1
0246
1
0072
Avg
1
0238
1
0192
1
0047
Min
1
0001
1
0002
1
0000
Lar
ge
pro
ble
ms
Max
1
0243
1
0178
1
0033
Avg
1
0050
1
0042
1
0005
Min
1
0001
1
0002
1
0000
r
Ov
eral
l p
rob
lem
s M
ax
102
82
102
46
100
72
0 -3A
vg
101
15
100
95
100
20
p Z
(Tab
le 3
Con
td)
0
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lgtT
able
3 (
Con
td)
Z
--------
_shy
SCG
PS
EQ
CG
PS
SCD
S SC
GP
S E
QC
GP
S SN
EH
SN
EH
EQ
IjE
H
SCD
S --
~ Jo
b C
DS
CDS
CG
PS
NEH
N
EH
CG
PS
CD
S CD
S N
EH
en
en
3 0
91
9
0
83
17
12
88
0
0 gtzj
4 2
8
7
11
2
70
28
21
76
3
gt-l
[1j
5 2
82
16
4
58
38
28
66
6
0 0 1
j6
3
77
20
1
51
48
40
56
4
en
7 5
75
20
5
43
52
45
50
5
~ 0 0
8 6
70
24
8
35
57
44
47
9
~
gt-l
9 5
6
7
28
2
40
58
46
46
8
10
7
68
25
5
40
55
42
50
8
15
7
70
23
2
45
53
46
50
4
20
8
65
27
4
46
50
37
58
5
30
8
74
18
0
57
43
35
63
2
40
7
72
21
3
53
44
29
62
9
50
13
62
25
4
49
47
31
59
10
10
0
15
6
0
25
1
52
47
27
71
2
20
0
17
64
19
2
55
43
26
68
6
30
0
12
74
14
7
63
30
17
74
9
w
(T
able
3 C
ontd
)
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Job
S
co
Ps
CD
S E
QC
GP
SC
DS
SCD
S C
GPS
SC
GP
S N
EH
E
QC
GP
SN
EH
S
NE
H
CG
PS
NE
H
SCD
S E
Q
EH
CD
S SC
DS
NE
Il
N
400
14
63
23
5
58
37
14
81
5
500
13
62
25
1
57
42
20
78
2
60
0
9
61
30
4
54
42
12
82
6
700
11
66
23
8
56
36
13
79
8
80
0
10
63
27
4
57
39
14
82
4
900
16
63
21
2
60
38
17
81
2
1000
11
61
28
4
57
39
12
82
6
Sm
all
pro
ble
ms
Min
Max
Avg
0
67
9
7
91
28
375
00
77
125
0
191
250
0
8
337
50
35
17
83
58
525
000
44
1250
12
46
347
500
46
88
598
750
0
9
537
50
Lar
ge
pro
ble
ms
Min
Max
7
17
60
74
14
30
0
8
45
63
30
53
12
46
50
82
2
10
114
00
65
3333
2
326
67
3
4000
546
000
42
0000
233
333
71
333
3
533
33
Ov
eral
l p
rob
lem
s
Min
Max
Avg
0
60
9
17
91
30
873
91
69
434
8 2
182
61
0
8
339
13
35
17
83
58
538
696
42
739
1
12
46
273
043
~~~--~--
46
0
88
10
673
478
5
3478
c 0 ~ gtshy L
0
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2014
343 ANALYSIS OF THE CGPS ALGORITHM
average-case performance ratio the NEH algorithm is the best one folshylowed by the CDS algorithm and then by the CGPS algorithm As comshypared with the CDS algorithm in terms of the proportional superiority 87391 of problem instances better by the CGPS algorithm 694348 of problem instances equal by the CGPS and CDS algorithms and 218261 of problem instances better by the CDS algorithm on the average It follows that the CDS algorithm is better than the CGPS alshygorithm especially for the large problems Similarly the NEH algorithm is better than the CGPS algorithm and the NEH algorithm is better than the CDS algorithm especially for the small problems Therefore we have the same conclusion about these heuristics in terms of the empirishycal average-case performance ratio and proportional superiority
To sum up 503478 ofthe initial permutation schedules generated by the CGPS algorithm are the optimal schedules Among the 496522 of non-optimal schedules 647154 of the permutation schedules 1t2 improve the makespan of the initial permutation schedule However the improvement made by 1t2 is only 03940 over 1tl in terms of the empirical average-case performance ratio Moreover compared with the NEH and CDS algorithms the CGPS algorithm is the worst one in terms of the empirical average-case performance ratio and proportional superiority These imply that the CGPS algorithm is not good enough for applications
5 CONCLUSION
In this paper the 3FSS with minimum makespan criterion is considered Theoretically the heuristic algorithm proposed by Chen et a1 [2] has the best worst-case performance ratio of 53 This paper evaluates the performance of the CGPS algorithm from the empirical perspective Three simulation experiments are performed to analyze its performance in terms of the six evaluation measures on a total of 13800 problems Four conclusions can be drawn from this paper Firstly in the CGPS algorithm 503478 of the initial permutation schedules are the optimal ones 321326 of the permutation schedshyules 1t2 improve the makespan of nl and 175196 of the permutation schedules n2 do not improve their makespan Secondly the empirical average-case performance ratio of initial permutation schedule genershyated by the CGPS algorithm is always smaller than the performance guarantee 53 developed by Chen et al [2] Its empirical average-case performance ratio is in the range of 10011 and 10355 and is 10156 on the average Thirdly the permutation schedule 1t2 improves 1tl in terms of the empirical average-case performance ratio by the non-significant percentage 03940 Fourthly among the three heuristic algorithms the
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344 H C TANG
NEH algorithm is the best one in terms of both empirical average-case performance ratio and proportional superiority followed by the CDS alshygorithm and then by the CGPS algorithm Consequently the CGPS algoshyrithm is not suitable for the 3FSS with minimum makespan criterion
REFERENCES
l H G Campbell R A Dudek and M L Smith A heuristic algorithm for the n-job tn-machine sequencing problem Management Science Vol 16 (1970) pp 630-637
2 B Chen C A Glass C N Potts and V A Strusevich A new heuristic for threeshymachine flow shop scheduling Operations Research Vol 44 (1996) pp 891-898
3 R W Conway W L Maxwell and L W Miller Theory of Scheduling AddisonshyWesley Reading MA 1967
4 R A Dudek S S Panwalkar and M L Smith The lessons of flowshop scheduling research Operations Research Vol 40 (1992) pp 7-13
5 M R Garey D S Johnson and R Sethi The complexity of flowshop and jobshop scheduling Mathematics Operations Research Vol 1 (1976) pp 117-129
6 S M Johnson Optimal two- and three-stage production schedules with setup times included Naval Research Logistic Quarterly Vol 1 (1954) pp 61-68
7 B J Lageweg J K Lenstra and A H G Rinnooy Kan A general bounding scheme for the permutation flow-shop problem Operations Research Vol 26 (1978) pp 53-67
8 E L Lawler J K Lenstra A H G Rinnooy Kan and D B Shmoys Sequencing and Scheduling Algorithms and Complexity in S C Graves A H G Rinnooy
-Kan and P H Zipkin Handbooks in Operations Research and Management Science Vol 4 Logistics of Production and Inventory North Holland Amsterdam 1993 pp 445-522
9 J K Lenstra A H G Rinnooy Kan and P Brucker Complexity of machine scheduling problems Annals Discrete Mathematics Vol 1 (1977) pp 343-362
10 M Nawaz E E Enscore Jr and I Ham A heuristic algorithm for the m-machine n-job flow-shop sequencing problem OMEGA Vol 11 (1983) pp 91-95
II C N Potts An Adaptive branching rule for the permutation flow-shop problem European Journal of Operational Research Vol 5 (1980) pp 19-25
12 A H G Rinnooy Kan Machine scheduling problems classification complexity and computations Nijhoff The Hague 1976
13 H Rock and G Schmidt Machine aggregation heuristics in shop scheduling Methods of Operations Research Vol 45 (1983) pp 303-314
14 C Smutnicki Some results of the worst-case analysis for flow shop scheduling European Journal of Operational Research Vol 109 (1998) pp 66-87
15 S M A Suliman A two-phase heuristic approach to the permutation flow-shop scheduling problem International Journal of Production Economics Vol 64 (2000) pp 143-152
16 H C Tang A new lower bonding rule for permutation flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 249-257
17 H C Tang A modification to the CGPS algorithm for three-machine flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 321-331
Received January 2001
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gtshyT
able
3
z C
om
par
ativ
e re
sult
s fo
r th
e C
GP
S
CD
S
an
d N
EH
alg
ori
thm
s ~
Job 3
4
5
6
7
8
9
10
15
20
30
40
50
100
200
101
39
101
48
102
64
102
82
102
62
102
7
102
82
102
55
102
43
101
83
101
06
100
85
100
65
100
32
100
17
~CDS
100
96
101
28
102
14
102
32
102
46
102
15
102
04
102
04
101
78
101
53
100
99
100
63
100
55
100
35
100
17
~~--------------
~NElI
100
1
100
39
100
53
100
54
100
48
100
72
100
50
100
47
100
25
100
33
100
08
100
05
100
06
100
02
100
01
(Tab
le 3
Con
td)
lfl
gt-
lfl
0 tJ r t=J
0 0 i
lfl gtshy r 0 0 ~ r $
w
w
to
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ber
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-----
---
co
jgt
~CGPS
~CDS
~NE
H 0
300
100
07
100
11
100
00
400
100
04
100
06
100
00
500
100
02
100
06
100
00
600
100
02
100
03
100
00
700
100
01
100
02
100
00
800
100
02
100
02
100
00
900
100
01
100
04
100
00
1000
1
0001
1
0003
1
0000
Min
1
0139
1
0096
1
0010
Sm
all
pro
ble
ms
Max
1
0282
1
0246
1
0072
Avg
1
0238
1
0192
1
0047
Min
1
0001
1
0002
1
0000
Lar
ge
pro
ble
ms
Max
1
0243
1
0178
1
0033
Avg
1
0050
1
0042
1
0005
Min
1
0001
1
0002
1
0000
r
Ov
eral
l p
rob
lem
s M
ax
102
82
102
46
100
72
0 -3A
vg
101
15
100
95
100
20
p Z
(Tab
le 3
Con
td)
0
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lgtT
able
3 (
Con
td)
Z
--------
_shy
SCG
PS
EQ
CG
PS
SCD
S SC
GP
S E
QC
GP
S SN
EH
SN
EH
EQ
IjE
H
SCD
S --
~ Jo
b C
DS
CDS
CG
PS
NEH
N
EH
CG
PS
CD
S CD
S N
EH
en
en
3 0
91
9
0
83
17
12
88
0
0 gtzj
4 2
8
7
11
2
70
28
21
76
3
gt-l
[1j
5 2
82
16
4
58
38
28
66
6
0 0 1
j6
3
77
20
1
51
48
40
56
4
en
7 5
75
20
5
43
52
45
50
5
~ 0 0
8 6
70
24
8
35
57
44
47
9
~
gt-l
9 5
6
7
28
2
40
58
46
46
8
10
7
68
25
5
40
55
42
50
8
15
7
70
23
2
45
53
46
50
4
20
8
65
27
4
46
50
37
58
5
30
8
74
18
0
57
43
35
63
2
40
7
72
21
3
53
44
29
62
9
50
13
62
25
4
49
47
31
59
10
10
0
15
6
0
25
1
52
47
27
71
2
20
0
17
64
19
2
55
43
26
68
6
30
0
12
74
14
7
63
30
17
74
9
w
(T
able
3 C
ontd
)
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ity o
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Job
S
co
Ps
CD
S E
QC
GP
SC
DS
SCD
S C
GPS
SC
GP
S N
EH
E
QC
GP
SN
EH
S
NE
H
CG
PS
NE
H
SCD
S E
Q
EH
CD
S SC
DS
NE
Il
N
400
14
63
23
5
58
37
14
81
5
500
13
62
25
1
57
42
20
78
2
60
0
9
61
30
4
54
42
12
82
6
700
11
66
23
8
56
36
13
79
8
80
0
10
63
27
4
57
39
14
82
4
900
16
63
21
2
60
38
17
81
2
1000
11
61
28
4
57
39
12
82
6
Sm
all
pro
ble
ms
Min
Max
Avg
0
67
9
7
91
28
375
00
77
125
0
191
250
0
8
337
50
35
17
83
58
525
000
44
1250
12
46
347
500
46
88
598
750
0
9
537
50
Lar
ge
pro
ble
ms
Min
Max
7
17
60
74
14
30
0
8
45
63
30
53
12
46
50
82
2
10
114
00
65
3333
2
326
67
3
4000
546
000
42
0000
233
333
71
333
3
533
33
Ov
eral
l p
rob
lem
s
Min
Max
Avg
0
60
9
17
91
30
873
91
69
434
8 2
182
61
0
8
339
13
35
17
83
58
538
696
42
739
1
12
46
273
043
~~~--~--
46
0
88
10
673
478
5
3478
c 0 ~ gtshy L
0
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343 ANALYSIS OF THE CGPS ALGORITHM
average-case performance ratio the NEH algorithm is the best one folshylowed by the CDS algorithm and then by the CGPS algorithm As comshypared with the CDS algorithm in terms of the proportional superiority 87391 of problem instances better by the CGPS algorithm 694348 of problem instances equal by the CGPS and CDS algorithms and 218261 of problem instances better by the CDS algorithm on the average It follows that the CDS algorithm is better than the CGPS alshygorithm especially for the large problems Similarly the NEH algorithm is better than the CGPS algorithm and the NEH algorithm is better than the CDS algorithm especially for the small problems Therefore we have the same conclusion about these heuristics in terms of the empirishycal average-case performance ratio and proportional superiority
To sum up 503478 ofthe initial permutation schedules generated by the CGPS algorithm are the optimal schedules Among the 496522 of non-optimal schedules 647154 of the permutation schedules 1t2 improve the makespan of the initial permutation schedule However the improvement made by 1t2 is only 03940 over 1tl in terms of the empirical average-case performance ratio Moreover compared with the NEH and CDS algorithms the CGPS algorithm is the worst one in terms of the empirical average-case performance ratio and proportional superiority These imply that the CGPS algorithm is not good enough for applications
5 CONCLUSION
In this paper the 3FSS with minimum makespan criterion is considered Theoretically the heuristic algorithm proposed by Chen et a1 [2] has the best worst-case performance ratio of 53 This paper evaluates the performance of the CGPS algorithm from the empirical perspective Three simulation experiments are performed to analyze its performance in terms of the six evaluation measures on a total of 13800 problems Four conclusions can be drawn from this paper Firstly in the CGPS algorithm 503478 of the initial permutation schedules are the optimal ones 321326 of the permutation schedshyules 1t2 improve the makespan of nl and 175196 of the permutation schedules n2 do not improve their makespan Secondly the empirical average-case performance ratio of initial permutation schedule genershyated by the CGPS algorithm is always smaller than the performance guarantee 53 developed by Chen et al [2] Its empirical average-case performance ratio is in the range of 10011 and 10355 and is 10156 on the average Thirdly the permutation schedule 1t2 improves 1tl in terms of the empirical average-case performance ratio by the non-significant percentage 03940 Fourthly among the three heuristic algorithms the
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344 H C TANG
NEH algorithm is the best one in terms of both empirical average-case performance ratio and proportional superiority followed by the CDS alshygorithm and then by the CGPS algorithm Consequently the CGPS algoshyrithm is not suitable for the 3FSS with minimum makespan criterion
REFERENCES
l H G Campbell R A Dudek and M L Smith A heuristic algorithm for the n-job tn-machine sequencing problem Management Science Vol 16 (1970) pp 630-637
2 B Chen C A Glass C N Potts and V A Strusevich A new heuristic for threeshymachine flow shop scheduling Operations Research Vol 44 (1996) pp 891-898
3 R W Conway W L Maxwell and L W Miller Theory of Scheduling AddisonshyWesley Reading MA 1967
4 R A Dudek S S Panwalkar and M L Smith The lessons of flowshop scheduling research Operations Research Vol 40 (1992) pp 7-13
5 M R Garey D S Johnson and R Sethi The complexity of flowshop and jobshop scheduling Mathematics Operations Research Vol 1 (1976) pp 117-129
6 S M Johnson Optimal two- and three-stage production schedules with setup times included Naval Research Logistic Quarterly Vol 1 (1954) pp 61-68
7 B J Lageweg J K Lenstra and A H G Rinnooy Kan A general bounding scheme for the permutation flow-shop problem Operations Research Vol 26 (1978) pp 53-67
8 E L Lawler J K Lenstra A H G Rinnooy Kan and D B Shmoys Sequencing and Scheduling Algorithms and Complexity in S C Graves A H G Rinnooy
-Kan and P H Zipkin Handbooks in Operations Research and Management Science Vol 4 Logistics of Production and Inventory North Holland Amsterdam 1993 pp 445-522
9 J K Lenstra A H G Rinnooy Kan and P Brucker Complexity of machine scheduling problems Annals Discrete Mathematics Vol 1 (1977) pp 343-362
10 M Nawaz E E Enscore Jr and I Ham A heuristic algorithm for the m-machine n-job flow-shop sequencing problem OMEGA Vol 11 (1983) pp 91-95
II C N Potts An Adaptive branching rule for the permutation flow-shop problem European Journal of Operational Research Vol 5 (1980) pp 19-25
12 A H G Rinnooy Kan Machine scheduling problems classification complexity and computations Nijhoff The Hague 1976
13 H Rock and G Schmidt Machine aggregation heuristics in shop scheduling Methods of Operations Research Vol 45 (1983) pp 303-314
14 C Smutnicki Some results of the worst-case analysis for flow shop scheduling European Journal of Operational Research Vol 109 (1998) pp 66-87
15 S M A Suliman A two-phase heuristic approach to the permutation flow-shop scheduling problem International Journal of Production Economics Vol 64 (2000) pp 143-152
16 H C Tang A new lower bonding rule for permutation flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 249-257
17 H C Tang A modification to the CGPS algorithm for three-machine flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 321-331
Received January 2001
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-----
---
co
jgt
~CGPS
~CDS
~NE
H 0
300
100
07
100
11
100
00
400
100
04
100
06
100
00
500
100
02
100
06
100
00
600
100
02
100
03
100
00
700
100
01
100
02
100
00
800
100
02
100
02
100
00
900
100
01
100
04
100
00
1000
1
0001
1
0003
1
0000
Min
1
0139
1
0096
1
0010
Sm
all
pro
ble
ms
Max
1
0282
1
0246
1
0072
Avg
1
0238
1
0192
1
0047
Min
1
0001
1
0002
1
0000
Lar
ge
pro
ble
ms
Max
1
0243
1
0178
1
0033
Avg
1
0050
1
0042
1
0005
Min
1
0001
1
0002
1
0000
r
Ov
eral
l p
rob
lem
s M
ax
102
82
102
46
100
72
0 -3A
vg
101
15
100
95
100
20
p Z
(Tab
le 3
Con
td)
0
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lgtT
able
3 (
Con
td)
Z
--------
_shy
SCG
PS
EQ
CG
PS
SCD
S SC
GP
S E
QC
GP
S SN
EH
SN
EH
EQ
IjE
H
SCD
S --
~ Jo
b C
DS
CDS
CG
PS
NEH
N
EH
CG
PS
CD
S CD
S N
EH
en
en
3 0
91
9
0
83
17
12
88
0
0 gtzj
4 2
8
7
11
2
70
28
21
76
3
gt-l
[1j
5 2
82
16
4
58
38
28
66
6
0 0 1
j6
3
77
20
1
51
48
40
56
4
en
7 5
75
20
5
43
52
45
50
5
~ 0 0
8 6
70
24
8
35
57
44
47
9
~
gt-l
9 5
6
7
28
2
40
58
46
46
8
10
7
68
25
5
40
55
42
50
8
15
7
70
23
2
45
53
46
50
4
20
8
65
27
4
46
50
37
58
5
30
8
74
18
0
57
43
35
63
2
40
7
72
21
3
53
44
29
62
9
50
13
62
25
4
49
47
31
59
10
10
0
15
6
0
25
1
52
47
27
71
2
20
0
17
64
19
2
55
43
26
68
6
30
0
12
74
14
7
63
30
17
74
9
w
(T
able
3 C
ontd
)
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ded
by [
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ity o
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t 22
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2014
Job
S
co
Ps
CD
S E
QC
GP
SC
DS
SCD
S C
GPS
SC
GP
S N
EH
E
QC
GP
SN
EH
S
NE
H
CG
PS
NE
H
SCD
S E
Q
EH
CD
S SC
DS
NE
Il
N
400
14
63
23
5
58
37
14
81
5
500
13
62
25
1
57
42
20
78
2
60
0
9
61
30
4
54
42
12
82
6
700
11
66
23
8
56
36
13
79
8
80
0
10
63
27
4
57
39
14
82
4
900
16
63
21
2
60
38
17
81
2
1000
11
61
28
4
57
39
12
82
6
Sm
all
pro
ble
ms
Min
Max
Avg
0
67
9
7
91
28
375
00
77
125
0
191
250
0
8
337
50
35
17
83
58
525
000
44
1250
12
46
347
500
46
88
598
750
0
9
537
50
Lar
ge
pro
ble
ms
Min
Max
7
17
60
74
14
30
0
8
45
63
30
53
12
46
50
82
2
10
114
00
65
3333
2
326
67
3
4000
546
000
42
0000
233
333
71
333
3
533
33
Ov
eral
l p
rob
lem
s
Min
Max
Avg
0
60
9
17
91
30
873
91
69
434
8 2
182
61
0
8
339
13
35
17
83
58
538
696
42
739
1
12
46
273
043
~~~--~--
46
0
88
10
673
478
5
3478
c 0 ~ gtshy L
0
Dow
nloa
ded
by [
Uni
vers
ity o
f T
enne
ssee
Kno
xvill
e] a
t 22
24 2
1 D
ecem
ber
2014
343 ANALYSIS OF THE CGPS ALGORITHM
average-case performance ratio the NEH algorithm is the best one folshylowed by the CDS algorithm and then by the CGPS algorithm As comshypared with the CDS algorithm in terms of the proportional superiority 87391 of problem instances better by the CGPS algorithm 694348 of problem instances equal by the CGPS and CDS algorithms and 218261 of problem instances better by the CDS algorithm on the average It follows that the CDS algorithm is better than the CGPS alshygorithm especially for the large problems Similarly the NEH algorithm is better than the CGPS algorithm and the NEH algorithm is better than the CDS algorithm especially for the small problems Therefore we have the same conclusion about these heuristics in terms of the empirishycal average-case performance ratio and proportional superiority
To sum up 503478 ofthe initial permutation schedules generated by the CGPS algorithm are the optimal schedules Among the 496522 of non-optimal schedules 647154 of the permutation schedules 1t2 improve the makespan of the initial permutation schedule However the improvement made by 1t2 is only 03940 over 1tl in terms of the empirical average-case performance ratio Moreover compared with the NEH and CDS algorithms the CGPS algorithm is the worst one in terms of the empirical average-case performance ratio and proportional superiority These imply that the CGPS algorithm is not good enough for applications
5 CONCLUSION
In this paper the 3FSS with minimum makespan criterion is considered Theoretically the heuristic algorithm proposed by Chen et a1 [2] has the best worst-case performance ratio of 53 This paper evaluates the performance of the CGPS algorithm from the empirical perspective Three simulation experiments are performed to analyze its performance in terms of the six evaluation measures on a total of 13800 problems Four conclusions can be drawn from this paper Firstly in the CGPS algorithm 503478 of the initial permutation schedules are the optimal ones 321326 of the permutation schedshyules 1t2 improve the makespan of nl and 175196 of the permutation schedules n2 do not improve their makespan Secondly the empirical average-case performance ratio of initial permutation schedule genershyated by the CGPS algorithm is always smaller than the performance guarantee 53 developed by Chen et al [2] Its empirical average-case performance ratio is in the range of 10011 and 10355 and is 10156 on the average Thirdly the permutation schedule 1t2 improves 1tl in terms of the empirical average-case performance ratio by the non-significant percentage 03940 Fourthly among the three heuristic algorithms the
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344 H C TANG
NEH algorithm is the best one in terms of both empirical average-case performance ratio and proportional superiority followed by the CDS alshygorithm and then by the CGPS algorithm Consequently the CGPS algoshyrithm is not suitable for the 3FSS with minimum makespan criterion
REFERENCES
l H G Campbell R A Dudek and M L Smith A heuristic algorithm for the n-job tn-machine sequencing problem Management Science Vol 16 (1970) pp 630-637
2 B Chen C A Glass C N Potts and V A Strusevich A new heuristic for threeshymachine flow shop scheduling Operations Research Vol 44 (1996) pp 891-898
3 R W Conway W L Maxwell and L W Miller Theory of Scheduling AddisonshyWesley Reading MA 1967
4 R A Dudek S S Panwalkar and M L Smith The lessons of flowshop scheduling research Operations Research Vol 40 (1992) pp 7-13
5 M R Garey D S Johnson and R Sethi The complexity of flowshop and jobshop scheduling Mathematics Operations Research Vol 1 (1976) pp 117-129
6 S M Johnson Optimal two- and three-stage production schedules with setup times included Naval Research Logistic Quarterly Vol 1 (1954) pp 61-68
7 B J Lageweg J K Lenstra and A H G Rinnooy Kan A general bounding scheme for the permutation flow-shop problem Operations Research Vol 26 (1978) pp 53-67
8 E L Lawler J K Lenstra A H G Rinnooy Kan and D B Shmoys Sequencing and Scheduling Algorithms and Complexity in S C Graves A H G Rinnooy
-Kan and P H Zipkin Handbooks in Operations Research and Management Science Vol 4 Logistics of Production and Inventory North Holland Amsterdam 1993 pp 445-522
9 J K Lenstra A H G Rinnooy Kan and P Brucker Complexity of machine scheduling problems Annals Discrete Mathematics Vol 1 (1977) pp 343-362
10 M Nawaz E E Enscore Jr and I Ham A heuristic algorithm for the m-machine n-job flow-shop sequencing problem OMEGA Vol 11 (1983) pp 91-95
II C N Potts An Adaptive branching rule for the permutation flow-shop problem European Journal of Operational Research Vol 5 (1980) pp 19-25
12 A H G Rinnooy Kan Machine scheduling problems classification complexity and computations Nijhoff The Hague 1976
13 H Rock and G Schmidt Machine aggregation heuristics in shop scheduling Methods of Operations Research Vol 45 (1983) pp 303-314
14 C Smutnicki Some results of the worst-case analysis for flow shop scheduling European Journal of Operational Research Vol 109 (1998) pp 66-87
15 S M A Suliman A two-phase heuristic approach to the permutation flow-shop scheduling problem International Journal of Production Economics Vol 64 (2000) pp 143-152
16 H C Tang A new lower bonding rule for permutation flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 249-257
17 H C Tang A modification to the CGPS algorithm for three-machine flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 321-331
Received January 2001
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Job
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343 ANALYSIS OF THE CGPS ALGORITHM
average-case performance ratio the NEH algorithm is the best one folshylowed by the CDS algorithm and then by the CGPS algorithm As comshypared with the CDS algorithm in terms of the proportional superiority 87391 of problem instances better by the CGPS algorithm 694348 of problem instances equal by the CGPS and CDS algorithms and 218261 of problem instances better by the CDS algorithm on the average It follows that the CDS algorithm is better than the CGPS alshygorithm especially for the large problems Similarly the NEH algorithm is better than the CGPS algorithm and the NEH algorithm is better than the CDS algorithm especially for the small problems Therefore we have the same conclusion about these heuristics in terms of the empirishycal average-case performance ratio and proportional superiority
To sum up 503478 ofthe initial permutation schedules generated by the CGPS algorithm are the optimal schedules Among the 496522 of non-optimal schedules 647154 of the permutation schedules 1t2 improve the makespan of the initial permutation schedule However the improvement made by 1t2 is only 03940 over 1tl in terms of the empirical average-case performance ratio Moreover compared with the NEH and CDS algorithms the CGPS algorithm is the worst one in terms of the empirical average-case performance ratio and proportional superiority These imply that the CGPS algorithm is not good enough for applications
5 CONCLUSION
In this paper the 3FSS with minimum makespan criterion is considered Theoretically the heuristic algorithm proposed by Chen et a1 [2] has the best worst-case performance ratio of 53 This paper evaluates the performance of the CGPS algorithm from the empirical perspective Three simulation experiments are performed to analyze its performance in terms of the six evaluation measures on a total of 13800 problems Four conclusions can be drawn from this paper Firstly in the CGPS algorithm 503478 of the initial permutation schedules are the optimal ones 321326 of the permutation schedshyules 1t2 improve the makespan of nl and 175196 of the permutation schedules n2 do not improve their makespan Secondly the empirical average-case performance ratio of initial permutation schedule genershyated by the CGPS algorithm is always smaller than the performance guarantee 53 developed by Chen et al [2] Its empirical average-case performance ratio is in the range of 10011 and 10355 and is 10156 on the average Thirdly the permutation schedule 1t2 improves 1tl in terms of the empirical average-case performance ratio by the non-significant percentage 03940 Fourthly among the three heuristic algorithms the
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344 H C TANG
NEH algorithm is the best one in terms of both empirical average-case performance ratio and proportional superiority followed by the CDS alshygorithm and then by the CGPS algorithm Consequently the CGPS algoshyrithm is not suitable for the 3FSS with minimum makespan criterion
REFERENCES
l H G Campbell R A Dudek and M L Smith A heuristic algorithm for the n-job tn-machine sequencing problem Management Science Vol 16 (1970) pp 630-637
2 B Chen C A Glass C N Potts and V A Strusevich A new heuristic for threeshymachine flow shop scheduling Operations Research Vol 44 (1996) pp 891-898
3 R W Conway W L Maxwell and L W Miller Theory of Scheduling AddisonshyWesley Reading MA 1967
4 R A Dudek S S Panwalkar and M L Smith The lessons of flowshop scheduling research Operations Research Vol 40 (1992) pp 7-13
5 M R Garey D S Johnson and R Sethi The complexity of flowshop and jobshop scheduling Mathematics Operations Research Vol 1 (1976) pp 117-129
6 S M Johnson Optimal two- and three-stage production schedules with setup times included Naval Research Logistic Quarterly Vol 1 (1954) pp 61-68
7 B J Lageweg J K Lenstra and A H G Rinnooy Kan A general bounding scheme for the permutation flow-shop problem Operations Research Vol 26 (1978) pp 53-67
8 E L Lawler J K Lenstra A H G Rinnooy Kan and D B Shmoys Sequencing and Scheduling Algorithms and Complexity in S C Graves A H G Rinnooy
-Kan and P H Zipkin Handbooks in Operations Research and Management Science Vol 4 Logistics of Production and Inventory North Holland Amsterdam 1993 pp 445-522
9 J K Lenstra A H G Rinnooy Kan and P Brucker Complexity of machine scheduling problems Annals Discrete Mathematics Vol 1 (1977) pp 343-362
10 M Nawaz E E Enscore Jr and I Ham A heuristic algorithm for the m-machine n-job flow-shop sequencing problem OMEGA Vol 11 (1983) pp 91-95
II C N Potts An Adaptive branching rule for the permutation flow-shop problem European Journal of Operational Research Vol 5 (1980) pp 19-25
12 A H G Rinnooy Kan Machine scheduling problems classification complexity and computations Nijhoff The Hague 1976
13 H Rock and G Schmidt Machine aggregation heuristics in shop scheduling Methods of Operations Research Vol 45 (1983) pp 303-314
14 C Smutnicki Some results of the worst-case analysis for flow shop scheduling European Journal of Operational Research Vol 109 (1998) pp 66-87
15 S M A Suliman A two-phase heuristic approach to the permutation flow-shop scheduling problem International Journal of Production Economics Vol 64 (2000) pp 143-152
16 H C Tang A new lower bonding rule for permutation flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 249-257
17 H C Tang A modification to the CGPS algorithm for three-machine flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 321-331
Received January 2001
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Job
S
co
Ps
CD
S E
QC
GP
SC
DS
SCD
S C
GPS
SC
GP
S N
EH
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QC
GP
SN
EH
S
NE
H
CG
PS
NE
H
SCD
S E
Q
EH
CD
S SC
DS
NE
Il
N
400
14
63
23
5
58
37
14
81
5
500
13
62
25
1
57
42
20
78
2
60
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61
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82
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700
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Sm
all
pro
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7
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eral
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rob
lem
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~~~--~--
46
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c 0 ~ gtshy L
0
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343 ANALYSIS OF THE CGPS ALGORITHM
average-case performance ratio the NEH algorithm is the best one folshylowed by the CDS algorithm and then by the CGPS algorithm As comshypared with the CDS algorithm in terms of the proportional superiority 87391 of problem instances better by the CGPS algorithm 694348 of problem instances equal by the CGPS and CDS algorithms and 218261 of problem instances better by the CDS algorithm on the average It follows that the CDS algorithm is better than the CGPS alshygorithm especially for the large problems Similarly the NEH algorithm is better than the CGPS algorithm and the NEH algorithm is better than the CDS algorithm especially for the small problems Therefore we have the same conclusion about these heuristics in terms of the empirishycal average-case performance ratio and proportional superiority
To sum up 503478 ofthe initial permutation schedules generated by the CGPS algorithm are the optimal schedules Among the 496522 of non-optimal schedules 647154 of the permutation schedules 1t2 improve the makespan of the initial permutation schedule However the improvement made by 1t2 is only 03940 over 1tl in terms of the empirical average-case performance ratio Moreover compared with the NEH and CDS algorithms the CGPS algorithm is the worst one in terms of the empirical average-case performance ratio and proportional superiority These imply that the CGPS algorithm is not good enough for applications
5 CONCLUSION
In this paper the 3FSS with minimum makespan criterion is considered Theoretically the heuristic algorithm proposed by Chen et a1 [2] has the best worst-case performance ratio of 53 This paper evaluates the performance of the CGPS algorithm from the empirical perspective Three simulation experiments are performed to analyze its performance in terms of the six evaluation measures on a total of 13800 problems Four conclusions can be drawn from this paper Firstly in the CGPS algorithm 503478 of the initial permutation schedules are the optimal ones 321326 of the permutation schedshyules 1t2 improve the makespan of nl and 175196 of the permutation schedules n2 do not improve their makespan Secondly the empirical average-case performance ratio of initial permutation schedule genershyated by the CGPS algorithm is always smaller than the performance guarantee 53 developed by Chen et al [2] Its empirical average-case performance ratio is in the range of 10011 and 10355 and is 10156 on the average Thirdly the permutation schedule 1t2 improves 1tl in terms of the empirical average-case performance ratio by the non-significant percentage 03940 Fourthly among the three heuristic algorithms the
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344 H C TANG
NEH algorithm is the best one in terms of both empirical average-case performance ratio and proportional superiority followed by the CDS alshygorithm and then by the CGPS algorithm Consequently the CGPS algoshyrithm is not suitable for the 3FSS with minimum makespan criterion
REFERENCES
l H G Campbell R A Dudek and M L Smith A heuristic algorithm for the n-job tn-machine sequencing problem Management Science Vol 16 (1970) pp 630-637
2 B Chen C A Glass C N Potts and V A Strusevich A new heuristic for threeshymachine flow shop scheduling Operations Research Vol 44 (1996) pp 891-898
3 R W Conway W L Maxwell and L W Miller Theory of Scheduling AddisonshyWesley Reading MA 1967
4 R A Dudek S S Panwalkar and M L Smith The lessons of flowshop scheduling research Operations Research Vol 40 (1992) pp 7-13
5 M R Garey D S Johnson and R Sethi The complexity of flowshop and jobshop scheduling Mathematics Operations Research Vol 1 (1976) pp 117-129
6 S M Johnson Optimal two- and three-stage production schedules with setup times included Naval Research Logistic Quarterly Vol 1 (1954) pp 61-68
7 B J Lageweg J K Lenstra and A H G Rinnooy Kan A general bounding scheme for the permutation flow-shop problem Operations Research Vol 26 (1978) pp 53-67
8 E L Lawler J K Lenstra A H G Rinnooy Kan and D B Shmoys Sequencing and Scheduling Algorithms and Complexity in S C Graves A H G Rinnooy
-Kan and P H Zipkin Handbooks in Operations Research and Management Science Vol 4 Logistics of Production and Inventory North Holland Amsterdam 1993 pp 445-522
9 J K Lenstra A H G Rinnooy Kan and P Brucker Complexity of machine scheduling problems Annals Discrete Mathematics Vol 1 (1977) pp 343-362
10 M Nawaz E E Enscore Jr and I Ham A heuristic algorithm for the m-machine n-job flow-shop sequencing problem OMEGA Vol 11 (1983) pp 91-95
II C N Potts An Adaptive branching rule for the permutation flow-shop problem European Journal of Operational Research Vol 5 (1980) pp 19-25
12 A H G Rinnooy Kan Machine scheduling problems classification complexity and computations Nijhoff The Hague 1976
13 H Rock and G Schmidt Machine aggregation heuristics in shop scheduling Methods of Operations Research Vol 45 (1983) pp 303-314
14 C Smutnicki Some results of the worst-case analysis for flow shop scheduling European Journal of Operational Research Vol 109 (1998) pp 66-87
15 S M A Suliman A two-phase heuristic approach to the permutation flow-shop scheduling problem International Journal of Production Economics Vol 64 (2000) pp 143-152
16 H C Tang A new lower bonding rule for permutation flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 249-257
17 H C Tang A modification to the CGPS algorithm for three-machine flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 321-331
Received January 2001
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343 ANALYSIS OF THE CGPS ALGORITHM
average-case performance ratio the NEH algorithm is the best one folshylowed by the CDS algorithm and then by the CGPS algorithm As comshypared with the CDS algorithm in terms of the proportional superiority 87391 of problem instances better by the CGPS algorithm 694348 of problem instances equal by the CGPS and CDS algorithms and 218261 of problem instances better by the CDS algorithm on the average It follows that the CDS algorithm is better than the CGPS alshygorithm especially for the large problems Similarly the NEH algorithm is better than the CGPS algorithm and the NEH algorithm is better than the CDS algorithm especially for the small problems Therefore we have the same conclusion about these heuristics in terms of the empirishycal average-case performance ratio and proportional superiority
To sum up 503478 ofthe initial permutation schedules generated by the CGPS algorithm are the optimal schedules Among the 496522 of non-optimal schedules 647154 of the permutation schedules 1t2 improve the makespan of the initial permutation schedule However the improvement made by 1t2 is only 03940 over 1tl in terms of the empirical average-case performance ratio Moreover compared with the NEH and CDS algorithms the CGPS algorithm is the worst one in terms of the empirical average-case performance ratio and proportional superiority These imply that the CGPS algorithm is not good enough for applications
5 CONCLUSION
In this paper the 3FSS with minimum makespan criterion is considered Theoretically the heuristic algorithm proposed by Chen et a1 [2] has the best worst-case performance ratio of 53 This paper evaluates the performance of the CGPS algorithm from the empirical perspective Three simulation experiments are performed to analyze its performance in terms of the six evaluation measures on a total of 13800 problems Four conclusions can be drawn from this paper Firstly in the CGPS algorithm 503478 of the initial permutation schedules are the optimal ones 321326 of the permutation schedshyules 1t2 improve the makespan of nl and 175196 of the permutation schedules n2 do not improve their makespan Secondly the empirical average-case performance ratio of initial permutation schedule genershyated by the CGPS algorithm is always smaller than the performance guarantee 53 developed by Chen et al [2] Its empirical average-case performance ratio is in the range of 10011 and 10355 and is 10156 on the average Thirdly the permutation schedule 1t2 improves 1tl in terms of the empirical average-case performance ratio by the non-significant percentage 03940 Fourthly among the three heuristic algorithms the
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344 H C TANG
NEH algorithm is the best one in terms of both empirical average-case performance ratio and proportional superiority followed by the CDS alshygorithm and then by the CGPS algorithm Consequently the CGPS algoshyrithm is not suitable for the 3FSS with minimum makespan criterion
REFERENCES
l H G Campbell R A Dudek and M L Smith A heuristic algorithm for the n-job tn-machine sequencing problem Management Science Vol 16 (1970) pp 630-637
2 B Chen C A Glass C N Potts and V A Strusevich A new heuristic for threeshymachine flow shop scheduling Operations Research Vol 44 (1996) pp 891-898
3 R W Conway W L Maxwell and L W Miller Theory of Scheduling AddisonshyWesley Reading MA 1967
4 R A Dudek S S Panwalkar and M L Smith The lessons of flowshop scheduling research Operations Research Vol 40 (1992) pp 7-13
5 M R Garey D S Johnson and R Sethi The complexity of flowshop and jobshop scheduling Mathematics Operations Research Vol 1 (1976) pp 117-129
6 S M Johnson Optimal two- and three-stage production schedules with setup times included Naval Research Logistic Quarterly Vol 1 (1954) pp 61-68
7 B J Lageweg J K Lenstra and A H G Rinnooy Kan A general bounding scheme for the permutation flow-shop problem Operations Research Vol 26 (1978) pp 53-67
8 E L Lawler J K Lenstra A H G Rinnooy Kan and D B Shmoys Sequencing and Scheduling Algorithms and Complexity in S C Graves A H G Rinnooy
-Kan and P H Zipkin Handbooks in Operations Research and Management Science Vol 4 Logistics of Production and Inventory North Holland Amsterdam 1993 pp 445-522
9 J K Lenstra A H G Rinnooy Kan and P Brucker Complexity of machine scheduling problems Annals Discrete Mathematics Vol 1 (1977) pp 343-362
10 M Nawaz E E Enscore Jr and I Ham A heuristic algorithm for the m-machine n-job flow-shop sequencing problem OMEGA Vol 11 (1983) pp 91-95
II C N Potts An Adaptive branching rule for the permutation flow-shop problem European Journal of Operational Research Vol 5 (1980) pp 19-25
12 A H G Rinnooy Kan Machine scheduling problems classification complexity and computations Nijhoff The Hague 1976
13 H Rock and G Schmidt Machine aggregation heuristics in shop scheduling Methods of Operations Research Vol 45 (1983) pp 303-314
14 C Smutnicki Some results of the worst-case analysis for flow shop scheduling European Journal of Operational Research Vol 109 (1998) pp 66-87
15 S M A Suliman A two-phase heuristic approach to the permutation flow-shop scheduling problem International Journal of Production Economics Vol 64 (2000) pp 143-152
16 H C Tang A new lower bonding rule for permutation flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 249-257
17 H C Tang A modification to the CGPS algorithm for three-machine flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 321-331
Received January 2001
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344 H C TANG
NEH algorithm is the best one in terms of both empirical average-case performance ratio and proportional superiority followed by the CDS alshygorithm and then by the CGPS algorithm Consequently the CGPS algoshyrithm is not suitable for the 3FSS with minimum makespan criterion
REFERENCES
l H G Campbell R A Dudek and M L Smith A heuristic algorithm for the n-job tn-machine sequencing problem Management Science Vol 16 (1970) pp 630-637
2 B Chen C A Glass C N Potts and V A Strusevich A new heuristic for threeshymachine flow shop scheduling Operations Research Vol 44 (1996) pp 891-898
3 R W Conway W L Maxwell and L W Miller Theory of Scheduling AddisonshyWesley Reading MA 1967
4 R A Dudek S S Panwalkar and M L Smith The lessons of flowshop scheduling research Operations Research Vol 40 (1992) pp 7-13
5 M R Garey D S Johnson and R Sethi The complexity of flowshop and jobshop scheduling Mathematics Operations Research Vol 1 (1976) pp 117-129
6 S M Johnson Optimal two- and three-stage production schedules with setup times included Naval Research Logistic Quarterly Vol 1 (1954) pp 61-68
7 B J Lageweg J K Lenstra and A H G Rinnooy Kan A general bounding scheme for the permutation flow-shop problem Operations Research Vol 26 (1978) pp 53-67
8 E L Lawler J K Lenstra A H G Rinnooy Kan and D B Shmoys Sequencing and Scheduling Algorithms and Complexity in S C Graves A H G Rinnooy
-Kan and P H Zipkin Handbooks in Operations Research and Management Science Vol 4 Logistics of Production and Inventory North Holland Amsterdam 1993 pp 445-522
9 J K Lenstra A H G Rinnooy Kan and P Brucker Complexity of machine scheduling problems Annals Discrete Mathematics Vol 1 (1977) pp 343-362
10 M Nawaz E E Enscore Jr and I Ham A heuristic algorithm for the m-machine n-job flow-shop sequencing problem OMEGA Vol 11 (1983) pp 91-95
II C N Potts An Adaptive branching rule for the permutation flow-shop problem European Journal of Operational Research Vol 5 (1980) pp 19-25
12 A H G Rinnooy Kan Machine scheduling problems classification complexity and computations Nijhoff The Hague 1976
13 H Rock and G Schmidt Machine aggregation heuristics in shop scheduling Methods of Operations Research Vol 45 (1983) pp 303-314
14 C Smutnicki Some results of the worst-case analysis for flow shop scheduling European Journal of Operational Research Vol 109 (1998) pp 66-87
15 S M A Suliman A two-phase heuristic approach to the permutation flow-shop scheduling problem International Journal of Production Economics Vol 64 (2000) pp 143-152
16 H C Tang A new lower bonding rule for permutation flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 249-257
17 H C Tang A modification to the CGPS algorithm for three-machine flow shop scheduling Journal of Information amp Optimization Sciences Vol 22 (2001) pp 321-331
Received January 2001
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