Richard A. Wysk

IE 551 – Computer Control in Manufacturing

Simulation-based Scheduling and Control

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

•Conceptualization•Preliminary Modeling•Systems Analysis•Detailing

Planning Manufacturing Systems

•Aggregate Visualization of System•No. of milling machines•No. of turning machines•...•...

•Arrangement of Machines•Layout•Location

Conceptualization

Operations Routing Operations Routing SummariesSummaries

Preliminary Modeling

Master Production Schedule

j

i

j MinutesinCapacity Weekly RequiredijOijD

jn Min.Available

jCapacity Required

A

Master Production Schedule

M1

M2

Mn

MH

PM1 PM2

PMn

Machine Requirements Analysis

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

Production Control ViewPart Perspective

M1 M2R

L UL

Controller determines what to do next.

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