Richard A. Wysk

1
Simulation-based Scheduling and Control

• Richard A. Wysk
• IE 551 Computer Control in Manufacturing

2
System vs. Simulation Modeling

• Purpose of Modeling
• Fidelity Level of Detail
• Constraints
• Cost
• Time
• Skilled People

System
Simulation Model
3
Different Uses of Manufacturing Simulation
Sales (cost/completion time prediction)
Product Design (DFM)
Process Planning
Maintenance
MRP (planning)
Facility Planning
Production Planning
Production Scheduling
System Design Analysis
Production Control
4
Factory Control - Observations
Most Analysis is for Processing Resources
Only Almost all Scheduling considers Processing
Resource Constraints Only There is no Material
Handling Planning
5
Different Uses vs. Associated Simulation Models
System Design Analysis
Production Scheduling
Production Control

• Chronological Uses of Simulation
• More specific and detailed, and higher fidelity
• More expensive and time-consuming to develop
• Shorter horizon (from months to seconds)

6
Simulation for Design Analysis
System Design Analysis
Production Scheduling
Production Control

• Traditional Usage of Simulation
• Before/after existence of a real system
• In general, no or little material handling detail
  -- time/cost constraints
• Results may not be always reliable when MHs are
  scarce resources
• Reference Smith et al., 1999

7
Planning Manufacturing Systems

• Conceptualization
• Preliminary Modeling
• Systems Analysis
• Detailing

8
Conceptualization

• Aggregate Visualization of System
• No. of milling machines
• No. of turning machines
• ...
• ...
• Arrangement of Machines
• Layout
• Location

9
Preliminary Modeling
Operations Routing Summaries
10
Master Production Schedule
11
Master Production Schedule
j
A
12
Machine Requirements Analysis
M1
PM1
PM2
MH
M2
Mn
PMn
13
Traditional Simulation
Nj -- no. of machines of type j Qj -- Queueing
character for machine j Wj -- Wait in j Ti --
Throughput time for part type i
14
Simulation for Scheduling
Production Control
System Design Analysis
Production Scheduling

• Traditionally after a real system has been
  designed (and typically built)
• Used for schedule generation or schedule
  evaluation
• Depending on systems, scheduling results vary
• Static Environments - Exact starting times and
  ending times
• Static/Dynamic Environments - work to schedules
  (lists)
• Dynamic Environments - scheduling strategies for
  each decision points
• With MH more expensive, but more accurate
  results
• Without MH easier to model, but difficult to
  implement schedules

15
Simulation for Control

• Traditionally after a real system has been
  designed (and typically built)
• Used for schedule generation or schedule
  evaluation
• Depending on systems, scheduling results vary
• Static Environments - Exact starting times and
  ending times
• Static/Dynamic Environments - work to schedules
  (lists)
• Dynamic Environments - scheduling strategies for
  each decision points
• With MH more expensive, but more accurate
  results
• Without MH easier to model, but difficult to
  implement schedules

16
MH devices

• Material Handling (MH)
• MH affects schedules
• MH is addressed every other process
• MH is frequently flexibility constraint

17
RapidCIM view to IllustrateControl Simulation
Requirements
3
6
4
5
2
7
1
M1
M2
R
8
UL
L
18
Resource AcquisitionSimulation for Real-time
Control
MH tasks are represented explicitly like MP
tasks Resource management is significantly complex
19
Some Observations about this Perspective

• Generic -- applies to any system
• Other application specifics
• Parts
• Number
• Routing
• Buffers (none in our system)

20
Deadlock Related References

• General deadlock discussions
• Wysk et al., 1994
• Cho et al., 1995
• Deadlock detection for simulation
• Venkatesh et al., 1998

21
Johnsons Algorithm (1954)

• Optimal sequence P1 - P3 - P4 - P2
• Is the schedule actually optimal in reality?

22
Traditional schedule v.s. Realistic schedule
(blocking effects)
1
3
4
2
M1
1
3
4
2
M2
Make-span 25
M1
Can not begin 4 until 3 moves
1
3
4
2
M2
1
3
4
2
Material Handling
Make-span 29
23
Actual optimal sequence
M1
1
3
4
2
M2
1
3
4
2
Optimum by Johnsons algorithm
Make-span 29
M1
1
2
3
4
M2
1
2
3
4
Actual optimum
Make-span 28
24
Things to be considered for higher fidelity of
scheduling

• Deadlocking and blocking related issues must be
  considered
• Material handling must be considered
• Buffers (and buffer transport time) must be
  considered

25
Jacksons Algorithm (1956)

• Optimal sequence
• M1 P1 - P2 - P3
• M2 P3 - P4 - P1
• Is the schedule actually optimal in reality?

26
Schedule Implementation

• If no buffers exist, it is impossible to
  implement the schedule as the optimum schedule by
  Jacksons rule
• Even if buffers exist, several better schedules
  may exist including the following schedule
• M1 P1 - P2 - P3
• M2 P1 - P3 - P4

27
Simulation specifics

• Very detailed simulation models that emulate the
  steps of parts through the system must be
  developed.
• Caution must be taken to insure that the model
  behaves properly.
• The simulation allocates resources (planning) and
  sequences activities (scheduling).

28
Why Acquire (seize) together?To avoid deadlock
P2 (M1-M2)
P1 (M1-M2)
M2
M1
part, being processed
part, done
Legend

• If we acquire robot and machine separately
• the robot will be acquired by the P2
• a deadlock situation will occur
• If we acquire robot and machine at the same time
• the robot will not be acquired until M2 becomes
  free

29
Time advancementSimulation for Design Analysis

• If the simulation runs in fast mode
• speed is subject to the computer performance
• speed is subject to animation complexity
• speed is subject to the frequency of events
• time delay is based on a statistical distribution
• e.g. Triangular (5,6,7)
• times are known in advance data collection

30
Time advancementSimulation for Real-time Control

• if runs in fast mode
• time delay is based on the expected processing
  time (typically a statistical distribution)
• Move to the next event as quickly as possible
• simulation time is based on the computer clock
  time
• time delay is based on the performance of a
  physical task (subject to machining parameters)
• task contains parameters task_name, part_id,
  op_id
• real-time system monitoring (animation)
• Reference Smith et al., 1994

31
Simulation can be used for control

• Traditionally run simulation in fast mode
• Can be coordinated to physical system via HLA or
  messaging

32
Production Control ViewPart Perspective
Controller determines what to do next.
33
Simulation-based Schedulingmethodologies

• Combinatorial approach -- intractable
• AI/Search algorithms
• Simulated annealing
• Tabu-search
• Genetic algorithm
• Neural networks (Cho and Wysk, 1993)
• Extended dispatching heuristics
• None of these guarantees optimization

34
Simulation-based Schedulingmulti-pass simulation

• Simulation
• real-time simulation - task generator
• fast simulation - schedule evaluator
• Who does the schedule generation then?
• Look ahead manager
• Scheduling come up with a good combination of
  control strategies for the decision points

35
Simulation-based Schedulingimplementation
parameters

• Performance measure
• Rescheduling point
• Simulation window (fast simulation length)
• Candidate alternatives
• Schedule results
• work to schedules for each equipment, or
• Control strategies
• Reference Wu and Wysk, 1989

36
Example system and associated connectivity graph
37
Generated Execution model -- based on the rules,
but manual yet
38
MPSG Summary
39
MPSG Summary
part_enter_sb
remove_kardex_sb
pick_ns_sb
0
1
2
3
return_sb
move_to_mach_sb
put_sb
move_to_kardex_sb
move_to_mach_sb
4
5
6
7
put_ns_sb
process_sb pick_sb
8
9
return_sb
40
Automatic Simulation Generation

• Motivation
• Simulation modeling is time-consuming
• Commonalities often appear within and between the
  models
• Preserving the fidelity between the models is
  important
• Automatic simulation model generation
• Based on a resource model and an execution model
• Information comprising each model
• General simulation model
• General resource model
• General execution model
• Implementation
• Arena real-time simulation
• MS Access 97 resource model
• MPSG execution model

41
Traditional system development vs. Models
automation approach
42
Traditional Simulation ApproachFor the
manufacturing system
System to be simulated
Manual Acquisition
Detailed specification
Programming
Simulation model
43
Automation Modeling Approach
System to be simulated
Domain Knowledge
Extraction Rules
Detailed specification
Target Language Knowledge
Construction Rules
Simulation model
44
System Description (extraction)
Natural Language
Graphical Formalism
Detailed Description
Dialog Monitor
User
Resource Model Process Model
Resource Model Execution Model
45
Information in Simulation

• Static information
• something like an experiment file
• resource information, shop layout
• Dynamic information
• part arrival process
• part flow and resource interaction
• Statistics needed
• resource utilization, throughput, etc

46
Penn State Simulation-based SFCS
47
Simulation-based Scheduling
Order Details
Remote Procedure Call
ARENA Real-time
Look-ahead Manager
Operating policy
"fastmode.bat" file
Dynamic Link Library
Database
ARENA fast-mode
Visual Basic Application
Process plans
Rule 1 Simulation
Rule n Simulation
Statistical Analysis
Best Rule Selection
48
Flow shop (m machines and m1 robots) -
non-synchronous control

• If no buffers exist, then we must allow blocking
  happen
• If buffers exist, there are three possible
  policies when blocking occurs
• Not picking up
• Picking up and waiting until the next machine
  becomes available,
• Picking up and moving it to the buffer
• Associated blocking control attributes are 1, 0,
  and 2, respectively
• We can specify above blocking control strategies
• Refer to the simulation construction rules in the
  next page

49
Information in Process Plans
For each part type ID, operation code,
description, resource_ID, Robot_location,
NC_file_name Reference Lee et al.,
1994 Implementation database representation PSL
(Process specification language) IDEF 3 (ICAM
Definition language) etc
50
Process Plan vs. Simulation

• Simulation in simulation based control
• Process plans reside externally
• Simulation in design and analysis
• Process plans reside within the simulation model
• Possible to include the alternative routings
  within the model

51
Conclusion

• Structure and information
• Simulation model
• Resource model
• Execution model
• Simulation model generation - resource model and
  execution model (blocking attributes)
• to be generated
• Depends on the types of system
• Pretty much for nothing

52
References

• Cho, H., T. K., Kumaran, and R. A. Wysk, 1995,
  Graph-theoretic deadlock detection and
  resolution for flexible manufacturing systems".
  IEEE Transactions on Robotics and Automation,
  Vol. 11, No. 3, pp. 413-421.
• Cho, H., and R. A. Wysk, 1993, "A Robust Adaptive
  Scheduler for an intelligent Workstation
  Controller". International Journal of Production
  Research, Vol. 31, No. 4, pp. 771-789.
• Drake, G.R., J.S. Smith, and B.A. Peters, 1995,
  "Simulation as a planning and scheduling tool for
  flexible manufacturing systems". Proceedings of
  the 1995 Winter Simulation Conference. pp.
  805-812.
• Ferreira, Joao C. and Wysk, R. A., An
  investigation of the influence of alternative
  process plans on equipment control, Journal of
  Manufacturing Systems, Vol. 19, No. 6, pp. 393
  406, 2001.
• Ferreira, J. C. E., Steele, J., Wysk, R. A., and
  Pasi, D. A., A Schema for Flexible Equipment
  Control in Manufacturing Systems, International
  Journal of Advanced Manufacturing Technology, Vol
  18, 410 - 421.
•  Lee, S., R. Wysk, and J. Smith, 1994, Process
  Planning Interface for a Shop Floor Control
  Architecture for Computer-integrated
  Manufacturing," International Journal of
  Production Research, Vol. 9, No. 9, pp. 2415 -
  2435.
• Smith, J. and S. Joshi., 1992, Message-based
  Part State Graphs (MPSG) A Formal Model for Shop
  Control, ASME Journal of Engineering for
  Industry, (In review).
• Smith, J., B. Peters, and A. Srinivasan, 1999,
  Job Shop scheduling considering material
  handling, International Journal of Production
  Research, Vol. 37, No. 7, 1541-1560

53
References

• Son, Young-Jun and Wysk, R. A., Automatic
  simulation model generation for simulation-based,
  real-time control, Computers in Industry, vol.
  45, pp 291 - 308, 2001.
• Steele, Jay W., Son, Young-Jun and Wysk, R. A.,
  Resource Modeling for Integration of the
  Manufacturing Enterprise, Journal of
  Manufacturing Systems, Vol. 19, No. 6, pp 407
  426, 2001.
• Moreno-Lizaranzu, Manuel J., Wysk, Richard A.,
  Hong, Joonki and Prabhu, Vittaldas V., A Hybrid
  Shop Floor Control System For Food
  Manufacturing, Transactions of IIE, Vol. 33, No.
  3, 193 2003, March 2001.
• Hong, Joonki, Prabhu Vittal and Wysk, R. A.,
  Real-time Batch Sequencing using arrival time
  control algorithm, International Journal of
  Production Research, Vol 39, No. 17, pp 3863
  3880, 2001.
• Ferreira, J. C. E. and Wysk, R. A., On the
  efficiency of alternative process plans, Journal
  of the Brazilian Society of Mechanical Sciences,
  Vol. XXIII, No. 3, pp 285 302, 2001.
• Smith, J. S., Wysk, R. A., Sturrok, D. T.,
  Ramaswamy, S. E., Smith, G. D., and S. B. Joshi.,
  1994, Discrete Event Simulation for Shop Floor
  Control Proceedings of the 1994 Winter
  Simulation Conference, pp. 962-969.
• Son, Y., H. Rodríguez-Rivera, and R. Wysk, 1999,
  A Multi-pass Simulation-based, Real-time
  Scheduling and Shop Floor Control System,"
  (Accepted) Transactions, The quarterly Journal of
  the Society for Computer Simulation International.

54
References

• Steele, J., S. Lee, C. Narayanan, and R. Wysk,
  1999, Resource Models for Modeling Product,
  Process and Production Requirements in
  Engineering Environments," submitted to
  International Journal of Production Research.
• Venkatesh, S., J. S. Smith, B. Deuermeyer, and G.
  Curry, 1998, Deadlock detection for discrete
  event simulation Multiple-unit seizes". IIE
  Transactions, Vol. 30 No. 3, pp. 201-216
• Wu, S.D. and R.A. Wysk, 1988, "Multi-pass expert
  control system - A control / scheduling structure
  for flexible manufacturing cells". Journal of
  Manufacturing Systems, Vol. 7 No. 2, pp. 107-120
• Wu, S.D. and R.A. Wysk, 1989, "An application of
  discrete-event simulation to on-line control and
  scheduling in flexible manufacturing".
  International Journal of Production Research,
  Vol. 27, No. 9, pp. 1603-1623.
• Wysk, R.A., Peters, B.A., and J.S. Smith, 1995,
  A Formal Process Planning Schema for Shop Floor
  Control Engineering Design and Automation
  Journal, Vol. 1, No. 1, pp. 3-19
• Wysk, R. A., N. Yang, S. Joshi, 1994, "Resolution
  of deadlocks in flexible manufacturing systems
  avoidance and recovering approaches". Journal of
  Manufacturing Systems, Vol. 13, No. 2, pp.
  128-138.