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CHAPTER 1

INTRODUCTION

1.1 MANUFACTURING SYSTEMS

Manufacturing is the economic term for making goods and services

available to satisfy human wants. Manufacturing implies creating value by applying

useful mental or physical labour. The manufacturing processes are collected

together to form a Manufacturing system. The manufacturing system takes inputs

and produces products for the customer. A manufacturing system can be defined as

“a collection of operations and processes used to obtain a desired product(s) or

component(s)”. A manufacturing system is therefore the design or arrangement of

the manufacturing processes (Paul Degarmo et al 2003).

1.2 CLASSIFICATION OF MANUFACTURING SYSTEMS

The manufacturing systems themselves differ in structure or physical

arrangement. According to the physical arrangement, there are four kinds of

classical manufacturing systems and two modern manufacturing systems that gain

acceptance in industries. The classical systems are

a. Job shop

b. Flow shop

c. Project shop

d. Continuous process.

Modern manufacturing systems are

a. Linked cell system or Cellular manufacturing system

b. Flexible manufacturing system

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1.2.1 Job Shop

Job shops are the most common manufacturing system. In general, job

shops are designed to achieve maximum flexibility so that a wide variety of

products with small lot sizes can be manufactured. Products manufactured in job

shops usually require different operations and have different operation sequences.

Operating time for each operation could vary significantly. Products are released to

the shops in batches (jobs). The requirements of the job shop - a variety of products

and small lot sizes - dictate what types of machines are needed and how they are

grouped and arranged. General-purpose machines are utilized in job shops because

they are capable of performing many different types of operations. Machines are

functionally grouped according to the general type of manufacturing process: lathes

in one department, drill presses in another, and so forth. Figure 1.1 illustrates a job

shop. A job shop layout can also be called a functional layout.

In job shops, jobs spend only 5% of their time in machines and the rest

of the time waiting or being moved from one functional area to the next (Paul

Degarmo et al 2003). When the processing of a part in the job shop has been

completed, it usually must be moved a relatively larger distance to reach the next

stage. It may have to travel the entire facility to complete all of the required

processes, as shown in Figure 1.1. Therefore, to make processing more economical,

parts are moved in batches. Each part in a batch must wait for the remaining parts in

its batch to complete processing before it is moved to the next stage. This leads to

longer production times, high levels of in-process inventory, high production costs

and low production rates.

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Fig 1.1 Job Shop Manufacturing

1.2.2 Flow Shop

The flow shop has a product oriented layout composed mainly of flow

lines. In contrast to job shops, flow lines are designed to manufacture high volumes

of products with high production rates and low costs. A flow line is organized

according to the sequence of operations required for a product. Specialized

machines, dedicated to the manufacture of the product, are utilized to achieve high

production rates. These machines are usually expensive; to justify the investment

cost of such machines, a large volume of the product must be produced. A major

limitation of flow lines is the lack of flexibility to produce products for which they

are not designed. This is because specialized machines are setup to perform limited

operations and are not allowed to be reconfigured. Figure 1.2 shows an example of

a flow line.

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Figure 1.2 Flow Line Manufacturing

1.2.3 Project Shop

In this, a product must remain in a fixed position or location because of

its size and weight. The materials, machines and people used in fabrication are

brought to the site. The layout is fixed position layout. Figure 1.3 a & b shows an

example of the project manufacturing.

a) Air craft assembly b) Ship building

Figure 1.3 Project manufacturing

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1.2.4 Continuous Process

In this continuous process, the project primarily deals with liquids, gases

(such as oil refineries, chemical process plants and food process industries) rather

than solids or discrete parts. It is the efficient but least flexible of the manufacturing

systems. This system is earliest to control having the least work in progress. Figure

1.4 a & b is an example for this type.

a) Oil refinery b) Food processing industry

Figure 1.4 a & b Continuous process manufacturing.

1.2.5 Linked Cell or Cellular Manufacturing System

Within the manufacturing system context, Group Technology (GT) is

defined as a manufacturing philosophy identifying similar parts and grouping them

together into families to take advantage of their similarities in design and

manufacturing. Cellular manufacturing is an application of group technology in

which dissimilar machines (or) processes have been aggregated into cells each of

which is dedicated to the production of a part (or) product family (or) a limited

group of families. Cellular Manufacturing (CM) involves the formation of part

families based upon their similar processing requirements and the grouping of

machines into manufacturing cells to produce the formed part families. A part

family is a collection of parts which are similar either because of geometric shape

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and size or similar processing steps required in their manufacture (Groover 1987). A

manufacturing cell consists of several functionally dissimilar machines, which are

placed in close proximity to one another and dedicated to the manufacture of a part

family.

The tenet of CM is to break up a complex manufacturing facility into

several groups of machines (cells), each being dedicated to the processing of a part

family. Therefore, each part type is ideally produced in a single cell. Thus, material

flow is simplified and the scheduling task is made much easier. As reported in the

survey by Wemmerlov and Johnson (1989), production planning and control

procedures have been simplified with the use of CM. The job shop in Figure 1.1 is

converted into a cellular manufacturing system (CMS) as shown in Figure 1.5.

Obvious benefits gained from the conversion of the shop are less travel distance for

parts, less space requirement, and need for fewer machines. Since similar part types

are grouped, this could lead to a reduction in setup time and allow a quicker

response to changing conditions. On the other hand, in the job shop, each part type

may have to travel through the entire shop; hence scheduling and materials control

are difficult. In addition, job priorities are complex to set and hence large

inventories are needed.

CM is a hybrid system linking the advantages of both job shops

(flexibility in producing a wide variety of products) and flow lines (efficient flow

and high production rate). In CM, machines are located in close proximity to one

another and dedicated to a part family. This provides the efficient flow and high

production rate similar to a flow line. The use of general-purpose machines and

equipments in CM allows machines to be changed in order to handle new product

designs and product demand with little efforts in terms of cost and time. So it

provides great flexibility in producing a variety of products.

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Figure 1.5 Cellular Manufacturing

In conclusion, CM is a manufacturing system that can produce medium-

volume / medium-variety part types more economically than other types of

manufacturing systems (Black 1983). If volumes are very large, pure item flow

lines are preferred; if volumes are small and part types are varied to the point of

only slight similarities between jobs, there is less to be gained by CM. The survey

by Wemmerlov and Johnson (1989) affirms that the greatest reported benefits from

CM appear along the dimension of time (manufacturing lead time and customer

response time). Thus, CM represents a logical choice for firms whose strategy is

time-based competitive manufacturing (Stalk and Hout 1990).

1.2.6 Flexible Manufacturing System

The FMS has established itself among the conventional production

systems as an efficient way to produce work pieces in medium lot sizes and medium

variety. It combines the merits of the job shop production and flow shop production.

The high level of automation previously reserved for mass production is now

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achievable for the medium sized production. The flexibility in manufacturing

enables companies to react fast on changing demands of the customers. FMS is

distinguished by the use of computer control in place of the hard automation usually

found in transfer lines. This enables FMS to reconfigure very rapidly to produce

multiple part types. Use of fixtures and tool magazines practically eliminates setup

time. These features permit economic production of large variety of parts in low

volumes.

A flexible manufacturing system integrates all major elements of

manufacturing into a highly automated system. The flexibility of FMS is such that it

can handle a variety of part configuration and produce them in any order. The basic

elements of FMS are a) works station b) automated material handling and AS/RS

systems c) control systems. Because of major capital investment efficient machine

utilization is essential. Consequently proper scheduling and process planning are

very complex. Because of the flexibility in FMS no setup time is wasted in between

manufacturing operations, the system is capable of different operations in different

orders and on different machines.

Figure 1.6 shows typical model of an FMS setup. Existing FMS

implementations have a number of benefits in terms of cost reductions, increased

utilizations, reduced work-in-process levels etc. However, there are a number of

problems faced during the life cycle of an FMS. These problems are classified into:

Design, Planning, and Scheduling and Control problems. In particular, the

scheduling task is of importance owing to the dynamic nature of the FMS such as

flexible parts, tools, AGV routings and Automated Storage and Retrieval System

(AS/RS) operation and control.

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Figure 1.6 Model of a Flexible Manufacturing System

1.3 SCHEDULING

Scheduling is considered to be a major task for shop floor productivity

improvement. Scheduling is the allocation of resources to apply the limiting factors

of time and cost to perform a collection of tasks. Scheduling theory is concerned

primarily with mathematical models that relate to the scheduling function and the

development of useful models and techniques. The objective function generally

consists of all costs in the system depending upon the type of scheduling decisions.

Frequently, an important cost-related measure of system performances (such as

makespan time, machine idle time, job waiting time, flow time of jobs and lateness

or tardiness or combination of these measures) can be used as a substitute for total

system cost.

Two kinds of feasibility constraints are commonly found in scheduling

problems. The first set of constraints is related to the amount of resource available

(like number of machines available in each type). The second set of constraints is

based on technological restrictions on the sequence in which tasks can be

performed.

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The objectives of the scheduling problem are listed below:

Determining the sequence in which tasks are to be performed.

Determining the start time and finish time of each task.

It is evident that the essence of scheduling is to make allocation

decisions pertaining to start and finish times for tasks. Scheduling can be classified

as given below:

Single Machine Scheduling (SMS)

Flow Shop Scheduling (FSS)

Job Shop Scheduling (JSS)

Cellular Manufacturing System Scheduling (CMSS)

Flexible Manufacturing System Scheduling (FMSS)

1.3.1 Single machine scheduling

In SMS, it is generally considered that n jobs (n = 1,2,3….n) are to be

processed on a single machine. The basic SMS problem is characterized by the

following conditions.

A set of independent, single-operation jobs are available for

processing at time zero.

Set-uptime of each job is independent of its position in jobs sequence

and hence this can be included in its processing time.

Job descriptors are known in advance.

One machine is continuously available and is never kept idle when

work is waiting.

Each job is processed till its completion without break.

The total number of sequences in the basic SMS problem is (n!), which

is the number of permutation of n elements. Due date (DD) is the time allowable

for completion of a job. If a job is not able to meet the DD, it is said to be late job

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or tardy job. Obviously the objective will be minimizing the tardiness or lateness.

Different time rules like shortest processing time, weighed mean flow time rule and

earliest due date rule are applied based on the objective function of the problem.

1.3.2 Flow shop scheduling

In FSS problems, there are n jobs; each requires processing of m

different machines. The order in which the machines are required to process a job

is called process sequence of that job. The process sequences of all the jobs are the

same. But the processing times for various jobs on a machine may differ. If an

operation is absent in job, the processing time of the operation of that job is

assumed as zero. The selection of an appropriate order of a set of jobs is known as

sequencing. In a flow shop, the machines are set up in series as the same processing

order of jobs. Every sequence of a set of jobs will have different performance

measures such as makespan time (total time required to complete processing of all

the jobs), total flow time (sum of actual time spent by all the jobs in the processing

environment), idle time of machines & jobs and tardiness (the positive lateness of

jobs beyond its due date) etc. It is difficult to suggest a sequence, which optimizes

all these performances together; rather these performances are purely independent

among themselves.

The FSS problem can be characterized as given below.

a) A set of multiple – operation jobs is available for processing at time

zero (Each job requires m operations and each operation requires a

different machine).

b) Set-up times for the operations are sequence independent, and are

included in processing times.

c) Job descriptors are known in advance.

d) M different machines are continuously available.

e) Each individual operation of jobs is processed till its completion

without break.

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The selection of an appropriate order of a set of jobs is known as

sequencing. In a flow shop, the machines are set up in series as the same processing

order of jobs. A sequence of a set of jobs will have different performance measures

such as makespan time, total flow time, idle time of machines & jobs and tardiness,

etc. It is difficult to suggest a sequence, which optimizes all these performances

together; rather these performances are purely independent among themselves.

1.3.2.1 General Flow shop

The general flow shop scheduling is one in which passing is allowed i.e.

a job can overtake another job while waiting in a queue to be processed by a

machine.

1.3.2.2 Permutation Flow shop

The permutation flow shop scheduling problem consists of scheduling n

jobs with given processing times on m machines, where the sequence of processing

a job on all machines is identical and unidirectional for each job. In studying flow

shop scheduling problems, it is a common assumption that the sequence in which

each machine processes all jobs is identical on all machines (permutation flow

shop). A schedule of this type is called a permutation schedule and is defined by a

complete sequence of all jobs.

1.3.3 Job shop Scheduling

In Job Shop Scheduling (JSS) problem, it is assumed that each job has m

different operations. If some of the jobs are having less than m operations, required

number of dummy operations with zero process times is assumed. By this

assumption, the condition of equal number of operations for all the jobs is ensured.

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In job shop scheduling problem, the process sequences of the jobs are not the same.

Hence, the flow of each job in JSS is not unidirectional. The time complexity

function of the JSS problem is combinatorial in nature. Hence, heuristic approaches

are popular in this area. Unlike the flow shop model, there is no initial machine that

neither performs only the first operation of a job, nor there a terminal machine that

performs only the last operation of a job. In job shop different jobs will have

different operations sequences. Hence, there is no straight flow of jobs in JSS.

Each operation j in the operation sequence of the job i in the JSS will be described

with triplet (i, j, k) where k is the required machine for processing the jth operation

on the ith job.

1.3.4 FMS Scheduling

The field of application for FMS concerning the product variety and lot

size fills the gap between the traditional production systems. Job shops and the

conventional equipment as shown in Figure 1.7 are characterized by a high variety,

low volume production and a random part flow. The random part flow results in a

relatively high risk of machine tools becoming idle due to a lack of parts to be

machined. This risk can be reduced by a high level of work-in-process. However,

the consequence of this measure is that, the lead times for a batch are usually high.

Flow lines (transfer line and special systems) on the other hand, are characterized

by a low variety, high volume production and a linear part flow. Linear part flow

results in a very low amount of work-in-process. Consequently, the machine

utilization is high and the lead time for work pieces is short. The purpose of the

FMS is to combine the flexibility of the job shop with the high work station

utilization. Scheduling in FMS is more complex than in classical machine shops. In

FMS, each operation of a part may be performed by any one of the several

machines. In other words the number of decision points where scheduling or

operation can be varied is greater in FMS than in job shops.

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Figure 1.7 Application Field of FMS

An FMS is a complex system which consists of a set of work stations,

Material Handling System (MHS) that connects these work stations by Automated

Guided Vehicle (AGV) and service centers (e.g., material warehouse, tool room,

repair equipment). The workstation is an autonomous unit that performs certain

manufacturing functions (e.g., a machining centre, inspection machine and a load-

unload robot). The MHS is used to distribute the appropriate input to the work

stations, so that the work station can perform its tasks and remove the output from

the work stations. To reduce the cost and increase production, the planning and

decision are made, such as balancing the workload of the workstations, scheduling

and dispatching, automated tool and material management.

1.3.5 CMS Scheduling

Due to the similarities in the design, shape, function, etc. parts in a part

family generally visit machines in the same sequence with minor differences in

setup requirements (Schaller 2001). Therefore, a part family can be divided into

several groups so that each group needs similar setup requirements. In other words,

a group is a sub set of a part family and all parts in the same group need similar set

up requirements. In group scheduling, it is assumed that each part family can be

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processed in one cell by duplicating bottleneck machines or subcontracting

exceptional parts (Logendran et al 1995). An exceptional part can be also be called

an exceptional element or a bottleneck part. A small example is given next in order

to introduce some of the terminology to be used in this dissertation. An example of

five part types and four machine types are used in order to form cells. A machine-

part matrix is one way to represent the processing requirements of part types on

machine types as shown in Table 1.1. A 1 entry on row i and column j indicates

that part type j has one or more operations on machine type i. For instance, part type

1 has operations on machine types 1 and 3. Manufacturing cells are formed with the

objective of minimizing inter cell moves. Two cells (clusters) are formed as shown

in Table 1.2. Cell 1 consists of machine type 2 and 4, and produces part type 5 and

2. Cell 2 consists of machine type 1 and 3, and produces part type 3, 1 and 4. Part

type 3 needs to be processed on machine type 1 and 3 in cell 2; however, it also

needs to be processed on machine type 2, which is assigned in cell 1. Therefore an

inter cell move is required: the symbol “*” represents an inter cell move of part type

3. Part type 3 is an exceptional part, so these two cells (clusters) are called partially

separable.

Analogous to an exceptional part, a bottleneck machine is one that

processes parts belonging to more than one cell. Two possible approaches to

eliminate exceptional parts are by considering alternative process plans for parts or

additional machines. In Table 1.2, 0 represents a void in cell 2. A void indicates that

a machine assigned to a cell is not required for the processing of a particular part in

the cell. In this example, machine type 3 is not necessary for part type 4. The

presence of voids leads to inefficient large cells, which in turn could lead to

additional intra cell material handling costs and complex control requirements.

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Table 1.1 Machine-Part Matrix Table 1.2 Cell Formation

Machine Type

Part Type Machine Type

Part Type 1 2 3 4 5 1 2 3 4 5

1 1 1 1 2 1 1 * 2 1 1 1 4 1 1 3 1 1 1 1 1 1 4 1 1 3 1 1 0

However, subcontracting exceptional parts may not be practical or

duplicating bottleneck machines may not be possible in every CMS environment

due to production economic, budget and manufacturing space limitations, etc. thus,

in typical CMS environment, it is difficult to form independent manufacturing cells

and mostly there are some exception parts that create inter-cellular moves (Shankar

and Vrat 1998). These constraints limit the applicability of group scheduling

method in real life.

1.4 TERMINOLOGIES

Processing time (tj): It is the time required to process job (j) on any machine. The

processing time (tj) will normally include both actual processing time and set-up

time.

Due date (dj): It is the time at which job (j) is to be completed.

Completion Time (Cj): It is the time at which the job (j) is completed in a

sequence. Performance measures like flow time, lateness and tardiness for

evaluating schedules are usually function of job completion time.

Flow time (Fj): It is the amount of time job (j) spends in the system. Flow time is a

measure of actual time spent by a job in the system. This in turn gives some idea

about in-process inventory of the shop floor.

Lateness time (Lj): It is the amount of time by which the completion time of job (j)

differs from the due date (Lj = Cj – dj). Lateness is a measure of non-conformity to

the due date. It may either be positive lateness or negative lateness. Positive

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lateness of the job is said to be lateness or tardiness and the negative lateness is

called as earliness. Therefore, it is often desirable to minimize the tardiness.

Tardiness (Tj): Tardiness is the lateness of job (j) if it fails to meet its due date and

it may either be zero or otherwise

Ti = max {Cj – dj}

1.5 MEASURE OF PERFORMANCES

Schedules are generally evaluated by aggregate quantities that involve

information about all jobs, resulting in one-dimensional performance measures.

Measures of a schedule performance are usually functions of the set of completing

times in a schedule. For example, suppose that n jobs are scheduled. The different

performance measures usually considered are listed below (Baker 1974).

Mean flow time : F =

n

jjFn

1/1

Mean Tardiness : T =

n

jjTn

1/1

Maximum flow time : }{max max jF

njlF

Maximum Tardiness : }{max max jT

njlT

Number of Tardy jobs :

n

jjT NfN

1)(

1.6 GENERAL STRUCTURE

1.6.1 FSS Problems

Let us consider the general structure of FSS problems of n jobs (n =

1,2,3…n), m machines (m = 1,2,3….m) with processing time tij represents the

processing time of ith machine. The general structure is shown in the Table 1.3.

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Table 1.3 General structures of FSS with n jobs and m machines.

Machines Jobs M1 M2 M3 . Mj. . Mm

J1 J2 .

Ji. .

Jn

t11 t12 t13 . t1j . t1m t21 t22 t23 . t2j . t2m . . . . . . . ti1 .ti2 .ti3 . tij . tim . . . . . . . tn1 tn2 tn3 . tnj. . tnm

The processing times of all the jobs are known and assumed that they are

processed in the same order on various machines.

1.6.2 Job shop scheduling problem

Each operation j in the operation sequence of the job i in the JSS

problem will be described with triplet (i,j,k) where k is the required machine for

processing the j th operation of the i th job. Let us consider the general structure of

JSS problems of 3 jobs, 3 operations and 3 machines. The general structure is

shown in the Table 1.4. The Table1.4 consists of machine sequence for each job

with processing time is given within the parenthesis.

Table 1.4 General structure of JSS.

Machine sequence (processing time in minutes)

Job 1 2 (6) 3(2) 1(7) Job 2 1(4) 3(9) 2(1) Job 3 3(5) 2(3) 1(4)

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1.7 OBJECTIVES

1.7.1 Makespan and Total flow time

Makespan is the time required to complete all the jobs. Let us consider

two machines and five-job problem with the assumption that the processing times

are known as shown in Table 1.5.

Table 1.5 2 Machines and 5 Jobs problems

Jobs

Machines j1 j2 j3 j4 j5

m1

m2

3 5 1 6 7

6 2 2 6 5

By applying Johnson’s rule (Johnson 1954) the sequence that optimizes

the makespan is 3 1 4 5 2. Computation of the entering and leaving time of jobs

with machines m1 and m2 with idle time of machines, m1 and m2 are shown in

Table 1.6.

Table 1.6 Computation of entering and leaving time of jobs on machine.

Jobs Machine (m1) Machine (m2) Idle

time of m1

Idle time of m2 in out in out

3 0 1 1 3 0 1 1 1 4 4 10 0 1 4 4 10 10 16 0 0 5 10 17 17 22 0 1 2 17 22 22 24 2 0 Total flow time 75

The total time required to complete all the five jobs is 24 time units,

known as makespan. Any other sequence other than this optimal sequence will

yield either equal or higher makespan. The total flow time of all the jobs will be the

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sum of all the flow time of jobs arrived in this example is 75. The mean flow time

is the mean of the total flow time.

1.7.2 Tardiness

In scheduling problems, when the jobs are considered with due dates, the

job which is completed beyond its due date is considered to be a tardy job. This

tardiness or the positive lateness of the jobs affect the production, by delayed

delivery time to the customer, the organization suffers with loss of reputation and

leads to customer dissatisfaction. Hence, tardiness of the jobs is to be minimized.

It is also one of the performance measures of the sequence. Many authors have

chosen this as an objective in FSS problems recently.

1.7.3 Multiple Objectives

The performance measures of FSS problems are mutually independent in

nature. In recent past, very few works have been reported with multiple criterions

of these performance measures. Different authors have tried different combinations

of these measures. However, the result proposed by multicriterion objectives of

FSS problems may not yield good result in any one particular criterion, instead it

minimizes more than one criterion so as to the minimize the total cost of

production.

1.8 NP-HARDNESS OF SCHEDULING PROBLEMS

Many authors have worked on optimizing for scheduling with various

objectives. In scheduling problems, considering n jobs and m machines, to suggest

a best sequence, (n!)m different sequences are to be examined with respect to any

performance measure. When the processing order is same for all jobs, the possible

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sequences will be reduced to (n!). This implies that for problems of larger size, the

searching region is increased exponentially, which results the scheduling problems

as NP hard problems or in other words the optimal solution will be in the region,

which is exponentially increasing. Scheduling problems come under combinatorial

category and the time taken to obtain optimum solution will be exponential in

nature. Hence, heuristic approaches are resorted to find acceptable solution in

reasonable time. In recent time many researchers in this area have developed their

heuristics by employing some of the exhaustive searching tools like, Genetic

Algorithm (GA), Ant System (AS), Simulate Annealing (SA), etc.

1.9 SCHEDULING OBJECTIVES

The scheduling is made to meet specific objectives. The objectives are

decided upon the situation, market demands, company demands and the customer’s

satisfaction. There are two broad categories for the scheduling objectives:

(i) Minimizing the makespan (ii) Due date based cost minimization.

The objectives considered under minimizing the makespan are,

(a) Minimize machine idle time

(b) Minimize the in process inventory costs

(c) Finish each job as soon as possible

(d) Finish the last job as soon as possible

The objectives considered under the due date based cost minimization are,

(a) Minimize the costs due to not meeting the due dates

(b) Minimize the maximum lateness of any job

(c) Minimize the total tardiness

(d) Minimize the number of late jobs

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1.10 SCHEDULING TECHNIQUES

There are a number of optimization techniques used for the scheduling.

The techniques are generally,

(i) Traditional Techniques

Mathematical Programming (Linear Programming, Integer

Programming, Goal Programming, Dynamic Programming, Transportation,

Network, Branch-and-Bound and Cutting Plane / Column Generation Method),

Heuristics Procedures (Priority Dispatching Rules, Composite Dispatching Rules),

Beam-Search, Enumerative Procedure, Decomposition (Lagrangian Relaxation),

McNaughton’s algorithm, Palmer’s heuristic, CDS algorithm, NEH algorithm,

Johnson algorithm etc.,

(ii) Non-Traditional Techniques

Evolutionary Programs (Genetic Algorithm, Particle Swarm

Optimization), Local Search Techniques (Ants Colony Optimization, Simulated

Annealing, Adaptive Search), scatter search and Artificial Intelligence Techniques

(Expert System, Artificial Neural Network) and Hybrid Techniques.

1.11 OBJECTIVE OF THE RESEARCH

The present research is focused on the scheduling problems on various

manufacturing systems with single and combined objective functions using non-

traditional techniques. The software codes have been developed using ‘C++’

language. The research scheme is depicted as shown in the Figure 1.8.

The research carried out encompasses the following objectives:

Non-traditional techniques are implemented for the problems of Scheduling

for various manufacturing systems

Evaluate the potential of Scatter search method for scheduling of various

manufacturing systems.

Comparison and analysis of the results.

Among the non-traditional techniques the superior one is identified.

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Figure 1.8 Research Scheme

Cellular Manufacturing

Minimizing the makespan

Minimizing Penalty Cost

Minimization of Weighted sum of makespan & total flow time

Single Machine

Flow Shop Job Shop FMS

Scheduling

Scheduling with CDD

Scheduling with CDW

General Scheduling

Permutation Scheduling

Minimizing the makespan

Minimizing the sum of the Earliness and Tardiness Penalties

Minimization of weighted sum of makespan & total flow time

Minimizing the makespan

Minimizing the machine idle time and total penalty

Implementation of Meta – heuristic methods

Results & Discussions

Conclusion

Scatter Search algorithms for scheduling of various manufacturing Systems

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1.12 ORGANIZATION OF THE THESIS

The present, chapter 1 gives bird’s eye view of various manufacturing

systems used and scheduling in general, single machine scheduling, flow shop

scheduling, job shop scheduling and scheduling on group technology concepts. Also

gives the research scheme presented in this research.

Chapter 2 presents the literature review done during the course of this research

work. It describes the research works reported by different authors in the field of

scheduling on various manufacturing systems with various objectives.

Chapter 3 entitled ‘Heuristics and metaheuristcs’ explain the details of heuristic and

metaheuristic methods used for solving scheduling problems. It also deals with the

metaheuristics used in this research.

Chapter 4 entitled ‘Single Machine Scheduling’ deals with the scheduling for Single

Machine Scheduling with Common Due Window and Common Due Date. The

compared results are presented with conclusion remarks.

Chapter 5 entitled ‘Flow Shop Scheduling’ describes the application of non-

traditional optimization techniques for solving the problem of flow shop scheduling

problems i.e. General and permutation flow shop with makespan objectives and

permutation flow shop with combined objectives. The results are compared with

results available in the literature and other methods used.

Chapter 6 entitled ‘Job Shop Scheduling’ details the scheduling of job shop problem

using Scatter search method and the results obtained is compared with various

metaheuristics results in the literature.

Chapter 7 entitled ‘Scheduling for Flexible Manufacturing Systems’ explains the

scheduling for FMS problems and the results obtained by Scatter search and ant

colony algorithm methods are compared with the results available in the literature.

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Chapter 8 entitled ‘Scheduling for Cellular Manufacturing Systems’ presents the

comparison and analysis of the results obtained by using non-traditional

optimization techniques for the cellular manufacturing scheduling problems with

objectives of penalty cost, makespan objective by considering inter cell and intra

cell movements and bi-criteria objectives.

Chapter 9 describes the conclusion of the research, which contains the outcome of

this research, limitations of this research work, and future research scope.

Appendix, references, list of publications and curriculum vitae are given

at the end.