richard a. wysk ie 551 – computer control in manufacturing simulation-based scheduling and control
TRANSCRIPT
System vs. Simulation Modeling
• Purpose of Modeling• Fidelity: Level of Detail• Constraints
CostTimeSkilled People
System
Simulation Model
Different Uses of Manufacturing Simulation
Production
Planning
Process Planning
Maintenance
Product Design(DFM)
ProductionSchedulin
gProductionControl
SystemDesign & Analysis
FacilityPlanning
Sales(cost/completion time prediction)
MRP(planning)
Most Analysis is for Processing Resources OnlyMost Analysis is for Processing Resources Only
Almost all Scheduling considers Processing Almost all Scheduling considers Processing Resource Constraints OnlyResource Constraints Only
There is no Material Handling PlanningThere is no Material Handling Planning
Factory Control - Observations
ProductionSchedulin
g
ProductionControl
SystemDesign & Analysis
Different Uses vs. Associated Simulation Models
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
ProductionSchedulin
g
ProductionControl
SystemDesign & Analysis
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
•Aggregate Visualization of System•No. of milling machines•No. of turning machines•...•...
•Arrangement of Machines•Layout•Location
Conceptualization
j
i
j MinutesinCapacity Weekly RequiredijOijD
jn Min.Available
jCapacity Required
A
Master Production Schedule
NNjj -- no. of machines of type j -- no. of machines of type j
QQjj -- Queueing character for machine j -- Queueing character for machine j
WWjj -- Wait in j -- Wait in j
TTii -- Throughput time for part type i -- Throughput time for part type i
Traditional Simulation
Simulation for Scheduling
ProductionScheduling
ProductionControl
SystemDesign & Analysis
•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 ControlProductio
nSchedulin
g
ProductionControl
SystemDesign & Analysis
•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
Material Handling (MH)Material Handling (MH) MH affects schedulesMH affects schedules
MH is addressed every other processMH is addressed every other process
MH is frequently flexibility constraintMH is frequently flexibility constraint
MH devices
RapidCIM view to IllustrateControl Simulation
Requirements
8
2
34 5
6
71
TaskNumber
TaskName
1 Pick L2 Put M13 Process 14 Pick M15 Put M26 Process 27 Pick M28 Put UL
M1 M2R
L 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)
Optimal sequence: P1 - P3 - P4 - P2 Is the schedule actually optimal in
reality?
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
Traditional schedule v.s. Realistic schedule (blocking effects)
1
1
3 4 2
3 4 2
Make-span: 25
M1
M2
1
1
3 4 2
3 4 2
Make-span: 29
M1
M2
+ Material Handling
Can not begin 4 until 3 moves
Actual optimal sequence
1
1
3 4 2
3 4 2
Make-span: 29
M1
M2
Optimum by Johnson’s algorithm
1
1
2 3 4
2 3 4
Make-span: 28
M1
M2
Actual optimum
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)
Optimal sequence: M1: P1 - P2 - P3 M2: P3 - P4 - P1
Is the schedule actually optimal in reality?
Operations Routing Summaries
Part # Sequence Times
1 M1 – M2 5 – 1
2 M1 4
3 M2 – M1 3 – 4
4 M2 2
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
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
:part, done :part, being processed
M1 M2
P2 (M1-M2) P1 (M1-M2)
Legend:
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 (subject 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
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
Part flow
Machine1 Machine3
Machine2
Robot
AS/RS 1
1
1
R
M2
M3
AS
1
Blocking Attribute
1: allowed0: not allowed
M1
Generated Execution model -- based on the rules, but manual yet
1
1
1
R
M2
M3
AS
1
Due to limited space, these two arrows are
expanded in this figure
part_enter@1_sb rm_asrs@1_sb rm@1_bk at_loc@1_kb
pick_ns#1@1_sb.......return_ok@1_bs
I I O I
II
at_loc@1_bs
O
pick_ns#1@1_br
O
mv_to_asrs@1_sb arrive@1_bk arrive_ok@1_kb loc_ok@1_bs
put_ns#1@1_sbput_ns#1@1_brclear_ok#1@1_rbput_ok#1@1_bs.......
I O I O
IOIO
T
delete@1
Robots IndexR 1
Stations IndexAS 1M1 2M2 3M3 4
Blocking attributes are set
to 1: must be blocked
M1
MPSG Summary part_enter@1_sb
0rm_asrs@1_sb pick_ns#1@1_sb
1 2 3
mv_to_mach@2_sb4
put#1@2_sb process@2_sb5 6 7
mv_to_mach@3_sb8
put#1@3_sb process@3_sb9 1
011
mv_to_mach@4_sb12
put#1@4_sb process@4_sb13
14
15
mv_to_asrs@1_sb16
put_ns#1@1_sb return#1@1_sb17
18
19
return@1_sb
pick#1@2_sb
pick#1@3_sb
pick#1@4_sb
MPSG Summary
part_enter_sb remove_kardex_sb pick_ns_sb return_sb
put_sb
move_to_mach_sb
move_to_kardex_sb
put_
ns_s
b
move_to_mach_sb
0 1 2 3
456
process_sbpick_sb
7
8 9return_sb
Traditional system development vs. Models automation approach
Multi-pass Simulation
Search-based Scheduling
Heuristic-based planning
A simple procedure
Manual generation
Manual generation
Shop level executor
Planner
Physical facility
Simulation (task generator)
Automatic generation
Automatic generation(Connectivity graph & rules)
Formal modeling &Database Instantiation
Shop level executor
Planner
Physical facility
Resource model
Simulation (task generator)
Scheduler
Associated with system development Associated with system operation
(a) Conventional Approach (b) Proposed Approach
Traditional Simulation Approach
For the manufacturing system
System to be simulated
Detailed specification
Simulation model
Manual Acquisition
Programming
Automation Modeling Approach
System to be simulated
Detailed specification
Simulation model
Extraction Rules
Construction Rules
Domain Knowledge
Target LanguageKnowledge
System Description (extraction)
Natural Language
Graphical Formalism
Dialog Monitor
Resource ModelProcess Model
Resource ModelExecution Model
UserDetailedDescription
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
Penn State Simulation-based SFCS
ARENA: real-time(Shop floor controller)
Big Executor (Shop Level)Big Executor (Shop Level)
Equipment Controllers
SL 20SL 20VF 0EVF 0EABB 2400
ABB 2400
PumaPumaMan MT
Man MT
KardexKardex
TaskOutput Queue
TaskOutput Queue
Database
Scheduler
TaskInput Queue
TaskInput Queue
ABB140
ABB140
Simulation-based Scheduling
Dynam
ic Link L
ibrary
Remote Procedure Call
Database
Statistical AnalysisStatistical Analysis
Best Rule SelectionBest Rule Selection
ARENA: Real-time
"fastmode.bat" file
ARENA: fast-mode
Visual Basic Application
Rule 1SimulationRule 1
Simulation
Rule nSimulationRule n
Simulation
Process plans
Look-ahead Manager
Operatingpolicy
Operatingpolicy
OrderDetailsOrder
Details
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
For each part typeID, operation code, description, resource_ID, Robot_location, NC_file_nameReference: Lee et al., 1994
Implementationdatabase representationPSL (Process specification language)IDEF 3 (ICAM Definition language)etc
Information in Process Plans
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
ReferencesSon, 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.
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.
References