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Intelligent Agent Based Model for an Industrial Order Picking Problem*. Byung-In Kim, Robert J. Graves and Sunderesh S. Heragu Rensselaer Polytechnic Institute Art St. Onge St. Onge Company May 20, 2001 * Supported by NSF Grant #DMI 9900039. IIE Annual Conference 2001. - PowerPoint PPT Presentation
Citation preview
Intelligent Agent Based Model
for an Industrial Order Picking Problem* Byung-In Kim, Robert J. Graves and Sunderesh S. Heragu
Rensselaer Polytechnic Institute
Art St. Onge
St. Onge Company
May 20, 2001
* Supported by NSF Grant #DMI 9900039IIE Annual Conference 2001
Presentation Outline
1. Manufacturing Control Frameworks
2. Industrial Order Picking Problem
3. Intelligent Pick-zone Assignment
4. Intelligent Conveyor Speed Adjustment
5. Conclusions & Discussion
2 / 14
1. Manufacturing Control Frameworks
Control components
Manufacturing entities
Hierarchical
Heterarchical
• Hierarchy: master/slave relationship• Structural rigidity• Difficulty of control system design• Lack of flexibility (Assume Deterministic)
• Interaction of autonomous components• Lack of global information• Difficulty in predicting system performance• Sensitivity to market rules
3 / 14
1. Manufacturing Control Frameworks
Hybrid
• Features of Hierarchical and Heterarchical• Hierarchy and Semi- Autonomous components• Globally optimized solution• Robustness against disturbances
4 / 14
2. Industrial Order Picking ProblemCosmetics Warehouse
Gantry Picking Complex (GPC): 16 pick zones
GPC characteristics: 65142 orders (116589 line items)/day
OAPS (Order Analysis and Planning System)
• Order Processing
• Create Next day’s order sequencing, pick plan,
and replenishment plan
FSS (Finite Scheduling System)
• Make detailed scheduling plan for gantry robots
• Execution of picking and replenishment
5 / 14
2. Industrial Order Picking Problem: GPC
Gantry Robot Pick tote Compartment
Conveyor Drop buffer(b) Picking Zone Layout
(a) GPC Layout
15 11 7 3
14 10 6 2
Sub-zone C Sub-zone B
1612 8 4
13 9 5 1
Sub-zone A
Sub-zone D
43
Pick Zone 12
87
56
1211
910
1615
1314
6 / 14
3. Intelligent Pick-zone Assignment
• Decision Maker: OAPS
• Method: Hierarchical & Static
• Decision Timing : One day
before picking
• Benefit: Ease of Calculation
• Problem: Separates Planning
from Execution
-> Lacks ability to handle
dynamic situation
Old Model New Model
• Decision Maker: FSS
• Method: Intelligent Agent Based
Hybrid & Dynamic
• Decision Timing: The moment
when the line-item enters
the system
• Benefit: Synchronized Planning
& Execution
-> Fault Tolerant -> Last minute changes to picking can be accommodated• Problem: More sophisticated calculation
7 / 14
3. Intelligent Pick-zone Assignment
Negotiation Protocol
Order AgentOrder Agent Pick-zone AgentPick-zone Agent
Task Announcement
Monitoring Bid-board
Select Best Bid
Delete Task, Bid
Confirm Task
Monitoring Task-board
Make a Bid
Bid Submission
Confirm Task
Task-Board
Bid-Board
)_,( timeLrobotgantrypicksduncompleteandcommittedfBidL 8 / 14
3. Intelligent Pick-zone Assignment
Simulation Results with various conveyor speeds
• Errors of the new model are always less than
the original hierarchical model
• Average utilization levels are almost the same
• Standard deviation of utilization levels are almost the same
H ier a r ch ica lM od e l
H ybr idM od e l
H ie r a r ch ica lM od e l
H ybr idM od e l
H ie r a r ch ica lM od e l
H ybr idM od e l
H ie r a r ch ica lM od e l
H ybr idM od e l
C on ve yor fe ed in ter va l 0 .8 0 .8 0 .7 5 0 .7 5 0 .7 0 .7 0 .6 5 0 .6 5N u m ber o f p ick e rr or s 0 0 4 0 5 1 2 0 5 2 3 3 6 7U ti l iza t ion m ean 7 3 .9 7 7 4 .7 8 7 8 .9 4 7 8 .9 2 8 4 .4 4 8 4 .5 1 9 0 .7 1 9 0 .8 1U ti l iza t ion std ev 0 .7 1 0 .7 0 0 .8 5 0 .6 2 0 .8 7 0 .7 1 0 .8 1 0 .6 2
C o n v e y o r F e e d In te rv a l v s . P ic k E r ro rs
0
2 0 0
4 0 0
6 0 0
0 . 8 0 0 . 7 5 0 . 7 0 0 . 6 5
C o n v e y o r I n t e r v a l
Erro
r
H i e r a r c h i c a l
H y b r i d
9 / 14
3. Intelligent Pick-zone Assignment
Utilization changing with breakdown
0.0
20.0
40.0
60.0
80.0
100.0
120.0
0 10000 20000 30000 40000 50000
Time (sec)
Uti
l. le
ve
l
G16
G15
G14
G13
G12
G11
G10
G9
G8
G7
G6
G5
G4
G3
G2
G1
• Flexibility and reconfigurability - machine breakdown scenario: G1-G4 down for 10,000 ~ 20,000 sec - the remaining 12 gantry robots are able to absorb the tasks of the down gantries if they have the needed SKUs
10 / 14
4. Intelligent Conveyor Speed Adjustment
Gantry Complex Agent
Gantry Agent
Gantry Agent
Gantry Agent
Gantry Agent
(1) Can conveyor speed up(down) ?
(2) Everybody is OK with new speed ?
(3) No problem(3) No I can’t
AP-Plex Agent
A-Plex Agent
Manual Agent
(4) I want to reset my conveyor speed. Is it OK to you ?
Negotiation Protocol
11 / 14
4. Intelligent Conveyor Speed Adjustment
(c) 0.75sec - 5min- 0.05sec
0.6
0.7
0.8
0.9
1
0 10000 20000 30000 40000 50000 60000
Time (sec)
(d) 0.75sec - 10min - 0.05sec
0.6
0.7
0.8
0.9
1
0 10000 20000 30000 40000 50000 60000
Time (sec)
(a) 0.75sec - 5min
0.6
0.7
0.8
0.9
1
0 10000 20000 30000 40000 50000 60000
Time (sec)
(b) 0.75sec - 10 min
0.6
0.7
0.8
0.9
1
0 10000 20000 30000 40000 50000 60000
Time (sec)
• More frequent oscillations in (a) than in (b)• Simple logic at the higher level agent to filter requests - Threshold 0.05 sec => Hybrid
* 0.75 sec : starting conveyor feed interval 5 min , 10 min : specified checking interval 0.05 sec : threshold for filtering logic of higher level agent (Gantry Complex agent)
12 / 14
4. Intelligent Conveyor Speed Adjustment
Fault Tolerance- Machine breakdown scenario: G1-G4 down for 10,000 ~ 20,000 sec- With dynamic speed adjustment, number of errors can be reduced
Hierarchical Model Hybrid with Static Speed Hybrid with Dynamic Speed# of picked 110374 113709 113925# of missed picks 6215 2880 2664# of errors after pick 954 81 6Total error 7169 2961 2670# of no-bids (irrecoverable) 2676 2516Gantry pick time 13:35 13:35 14:45Mean util. % 74.80 76.12 71.74STD util. 6.64 8.32 6.91
Using Naive Conservative Logic- Mean Utilization with Dynamic Speed < Mean utilization with Static Speed => conservative
13 / 14
5. Conclusions & Discussions
• Intelligent agent based hybrid model for actual
industrial problem
• Resource assignment problem and dynamic conveyor
speed adjustment
• Hybrid model outperforms pure hierarchical and
heterarchical models
Conclusions
• Hybrid Scheduling and Control System Architecture for
Robustness and Global Optimization
• Guidelines for designing intelligent agent based production/
warehousing planning, scheduling, and control systems
• Chaos concerns in manufacturing
In Progress & Future Works
14 / 14
Hybrid Scheduling and Control System Architecture
Machine/ MHD Agent
PartAgent
Bulletin Board
Higher Level Global Optimizer Agent
Middle Level Guide Agent
Work Load Balancing between Order Trains
Order-Stream is made by OAPSWork Load between trains is unbalanced
Histogram
0204060
80100120140
Bin
Freq
uenc
y
Min 83Max 235Average 143.13Std Dev 17.44Total train # 815
Lack of OAPS => Developed a PreprocessorPair wise exchange between non zip-qualified orders
Min 140Max 146Average 143.11Std Dev 1.84Total train # 815
Histogram
0
50
100
150
200
140.0 141.1 142.1 143.0 144.1 145.1 More
BinFr
eque
ncy
Train Line-Item Number
0
50
100
150
200
250
1 48
95
142
189
236
283
330
377
424
471
518
565
612
659
706
753
800
Train number
Lin
e-I
tem
Nu
mb
er
Train-Line #
0
50
100
150
200
250
1 41 81 121
161
201
241
281
321
361
401
441
481
521
561
601
641
681
721
761
801
Train #
Lin
e It
em
#
Work Load Balancing between Order TrainsSimulation Results with various conveyor speeds
Conveyor Original Bidding Balanced BalancedInterval Error Error Original Bidding
Error Error0.80 0 0 0 00.75 4 0 0 00.70 51 20 9 00.65 523 367 191 39
Conveyor Speed Vs. Error
0
100
200
300
400
500
600
0.80 0.75 0.70 0.65
Conveyor Interval(sec)
Err
or
#
Original
Bidding
Balanced-Original
Balanced-Bidding
• Throughput improvement in the system by balancing workload and using bidding, 12.5 %(0.10/0.80) easily seen