Simulation-based
Scheduling and Control
Richard A. Wysk
IE 551 – Computer Control in
Manufacturing
System vs. Simulation
Modeling
System
Simulation Model
• Purpose of Modeling
• Fidelity: Level of Detail
• Constraints
Cost
Time
Skilled People
Different Uses of Manufacturing
Simulation
Sales
(cost/completion
time prediction)
Product Design
(DFM)
Process Planning
Maintenanc
e
MRP
(planning)
Facility
Planning
Productio
n
Planning
System
Design &
Analysis
Production
Control
Production
Schedulin
g
Factory Control - Observations
Most Analysis is for Processing Resources Only
Almost all Scheduling considers Processing Resource
Constraints Only
There is no Material Handling Planning
Different Uses vs. Associated
Simulation Models
System
Design &
Analysis




Production
Schedulin
g
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)
Simulation for Design &
Analysis
System
Design &
Analysis



Production
Schedulin
g
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
Planning Manufacturing
Systems
•Conceptualization
•Preliminary Modeling
•Systems Analysis
•Detailing
Conceptualization
•Aggregate Visualization of System
•No. of milling machines
•No. of turning machines
•...
•...
•Arrangement of Machines
•Layout
•Location
Preliminary Modeling
Operations Routing Summaries
Part #
1
2
3
4
.
.
.
Routing
M1 - M2 - M3 - M4
M2 - M3
M3 - M4
M3 - M1 - M2
Time Required
5-3-2-1
6-4
5-3
2-4-3
Master Production Schedule
Weekly Demand
Product #
1
2
3
4
500
10,000
1,000
400
Master Production Schedule
j
DijOij  Required WeeklyCapacityin Minutesj
i
Required Capacityj
Available Min.

 nj
Machine Requirements Analysis
M1
M2
Mn
PM1
PM2
MH
PMn
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
Simulation for Scheduling
System
Design &
Analysis
Production
Scheduling
Production
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
Simulation for Control
System
Design &
Analysis
Productio
n
Schedulin
g
Production
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
MH devices
Material Handling (MH)
 MH affects schedules
 MH is addressed every other process
 MH is frequently flexibility constraint
RapidCIM view to Illustrate
Control Simulation Requirements
3
6
4
M1
2
1
L
5
7
R
M2
8
UL
Task
Number
1
2
3
4
5
6
7
8
Task
Name
Pick L
Put M1
Process 1
Pick M1
Put M2
Process 2
Pick M2
Put UL
Some Observations about this
Perspective


Generic -- applies to any system
Other application specifics

Parts



Number
Routing
Buffers (none in our system)
Deadlock Related References

General deadlock discussions



Wysk et al., 1994
Cho et al., 1995
Deadlock detection for simulation

Venkatesh et al., 1998
Johnson’s Algorithm (1954)
Operations Routing Summaries for a family of parts (M1 – M2)


Part
P1
P2
P3
P4
M1
2
8
4
7
M2
9
3
5
6
Optimal sequence: P1 - P3 - P4 - P2
Is the schedule actually optimal in reality?
Traditional schedule v.s.
Realistic schedule (blocking effects)
M1
M2
1
3
4
1
2
3
4
2
Make-span: 25
M1
M2
1
3
Can not begin 4
until 3 moves
1
4
3
+ Material Handling
2
4
2
Make-span: 29
Actual optimal sequence
M1
M2
1
3
4
1
2
3
4
Optimum by Johnson’s algorithm
M1
M2
1
2
3
1
Actual optimum
2
2
Make-span: 29
4
3
4
Make-span: 28
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
Jackson’s Algorithm (1956)
Operations Routing Summaries

Sequence
Times
1
M1 – M2
5–1
2
M1
4
3
M2 – M1
3–4
4
M2
2
Optimal sequence:



Part #
M1: P1 - P2 - P3
M2: P3 - P4 - P1
Is the schedule actually optimal in reality?
Schedule Implementation


If no buffers exist, it is impossible to
implement the schedule as the optimum
schedule by Jackson’s rule
Even if buffers exist, several better schedules
may exist including the following schedule:


M1: P1 - P2 - P3
M2: P1 - P3 - P4
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).
Why Acquire (seize) together?
To avoid deadlock
P2 (M1-M2)
M2
M1
Legend:

:part, done
:part, being processed
If we acquire robot and machine separately



P1 (M1-M2)
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
Time advancement:
Simulation 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 (subjec
to machining parameters)
task contains parameters: task_name, part_id, op_id
real-time system monitoring (animation)
Reference: Smith et al., 1994
Simulation can be used for
control


Traditionally run simulation in fast mode
Can be coordinated to physical system
via HLA or messaging
Production Control View
Part Perspective
M1
M2
R
Controller determines
what to do next.
L
UL
Simulation-based Scheduling:
methodologies


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
Simulation-based Scheduling:
multi-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
Example system and associated connectivity
graph
M2
1
Machine2
Machine1
Machine3
Robot
Part flow
AS/RS
M1
R
1
Blocking
Attribute
1: allowed
0: not allowed
M3
1
1
AS
Generated Execution model -- based on the rules, but
manual yet
M
2
Blocking
attributes are set
to 1: must be
blocked
1
1
M
1
R
M
3
1
Due to limited space,
these two arrows are
expanded in this
figure
part_enter@1_sb
I
1
A
S
rm_asrs@1_sb
I
.......
mv_to_asrs@1_sb
I
.......
put_ok#1@1_bs
O
Stations
AS
M1
M2
M3
Index
1
2
3
4
O
pick_ns#1@1_br
pick_ns#1@1_sb
O
arrive@1_bk
arrive_ok@1_kb
O
I
clear_ok#1@1_rb
I
at_loc@1_bs
I
I
T
Index
1
at_loc@1_kb
O
return_ok@1_bs
delete@1
rm@1_bk
Robots
R
I
loc_ok@1_bs
O
put_ns#1@1_br
put_ns#1@1_sb
O
I
MPSG Summary
part_enter@1_sb
0
rm_asrs@1_sb
1
pick_ns#1@1_sb
2
3
return@1_sb
mv_to_mach@2_sb
4
put#1@2_sb
5
process@2_sb
6
7
pick#1@2_sb
mv_to_mach@3_sb
8
put#1@3_sb
9
process@3_sb
1
0
1
1
pick#1@3_sb
mv_to_mach@4_sb
1
2
put#1@4_sb
1
3
process@4_sb
1
4
1
5
pick#1@4_sb
mv_to_asrs@1_sb
1
6
put_ns#1@1_sb
1
7
return#1@1_sb
1
8
1
9
MPSG
Summary
part_enter_sb
1
remove_kardex_sb
2
pick_ns_sb
3
put_ns_sb
move_to_mach_sb
7
move_to_kardex_sb
6
put_sb
process_sb
pick_sb
8
return_sb
9
5
move_to_mach_sb
4
return_sb
0
Traditional system development vs. Models automation
approach
Physical facility
Physical facility
Formal modeling &
Database Instantiation
Manual generation
Shop level executor
Resource model
Automatic generation
(Connectivity graph & rules)
Manual generation
Simulation (task generator)
Shop level executor
Automatic generation
A simple procedure
Planner
Simulation (task generator)
Heuristic-based
planning
Multi-pass
Simulation
Planner
Associated with system development
(a) Conventional Approach
Search-based
Scheduling
Scheduler
Associated with system operation
(b) Proposed Approach
Traditional Simulation Approach
For the manufacturing system
System to be simulated
Manual Acquisition
Detailed specification
Programming
Simulation model
Automation Modeling Approach
System to be simulated
Extraction Rules
Domain
Knowledge
Detailed specification
Construction Rules
Simulation model
Target Language
Knowledge
System Description (extraction)
Natural Language
Graphical Formalism
User
Dialog Monitor
Resource Model
Process Model
Resource Model
Execution Model
Detailed
Description
Information in Simulation

Static information



Dynamic information



something like an experiment file
resource information, shop layout
part arrival process
part flow and resource interaction
Statistics needed

resource utilization, throughput, etc
Penn State Simulation-based SFCS
Scheduler
Databas
e
ARENA: real-time
(Shop floor
controller)
Task
Output
Queue
Kardex
ABB
140
Task
Input Queue
Big Executor (Shop Level)
Man
MT
ABB
2400
VF 0E
Equipment Controllers
SL 20
Puma
Simulation-based Scheduling
Order
Details
Look-ahead Manager
Remote Procedure Call
Operating
policy
"fastmode.bat" file
Dynamic
Link
Library
ARENA: fast-mode
Visual Basic Application
Rule 1
Simulation
Rule n
Simulation
Statistical Analysis
Best Rule Selection
ARENA: Real-time
Database
Process
plans
Flow shop (m machines and m+1 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
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
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
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
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
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.

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. 128138.