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 Cost Time Skilled People System Simulation Model

Different Uses of Manufacturing Simulation Productio n Planning Process Planning Maintenanc e Product Design (DFM) Production Schedulin g Production Control System Design & Analysis Facility Planning Sales (cost/completion time prediction) MRP (planning)

Most Analysis is for Processing Resources Only Almost all Scheduling considers Processing Resource Constraints Only There is no Material Handling Planning Factory Control - Observations

Production Schedulin g Production Control System Design & 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 Production Schedulin g Production Control System Design & 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 Summaries Preliminary Modeling

Master Production Schedule

j A

M1M1 M2M2 MnMn MH P M1 P M2 P Mn Machine Requirements Analysis

N j -- no. of machines of type j Q j -- Queueing character for machine j W j -- Wait in j T i -- Throughput time for part type i Traditional Simulation

Simulation for Scheduling Production Scheduling Production Control System Design & 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 Control Productio n Schedulin g Production Control System Design & 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)  MH affects schedules  MH is addressed every other process  MH is frequently flexibility constraint MH devices

RapidCIM view to Illustrate Control Simulation Requirements Task Number Task Name 1Pick L 2Put M1 3Process 1 4Pick M1 5Put M2 6Process 2 7Pick M2 8Put UL M1M2 R L UL

Resource Acquisition: Simulation for Real-time Control MH tasks are represented explicitly like MP tasks Resource management is significantly complex Task Number Task Name M1M2R 1Pick L  2Put M1  3Process 1  4Pick M1  5Put M2  6Process 2  7Pick M2  8Put U  

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?

Traditional schedule v.s. Realistic schedule (blocking effects) Make-span: 25 M1 M Make-span: 29 M1 M2 + Material Handling Can not begin 4 until 3 moves

Actual optimal sequence Make-span: 29 M1 M2 Optimum by Johnson’s algorithm 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?

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 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

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 View Part Perspective M1M2 R 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

Simulation-based Scheduling: implementation 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

Example system and associated connectivity graph Part flow Machine1Machine3 Machine2 Robot AS/RS R M2 M3 AS 1 Blocking Attribute 1: allowed 0: not allowed M1

Generated Execution model -- based on the rules, but manual yet R M2M2 M3M3 ASAS 1 Due to limited space, these two arrows are expanded in this figure IIOI II O O IOIO IOIO T Robots Index R1 Stations Index AS1 M12 M23 M34 Blocking attributes are set to 1: must be blocked M1M1

MPSG Summary

MPSG Summary part_enter_sbremove_kardex_sbpick_ns_sb return_sb put_sb move_to_mach_sb move_to_kardex_sb put_ns_sb move_to_mach_sb process_sb pick_sb 7 89 return_sb

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

Traditional system development vs. Models automation approach Multi-pass Simulation Search-based Scheduling Heuristic-based planning A simple procedure Manual generation Shop level executor Planner Physical facility Simulation (task generator) Automatic generation (Connectivity graph & rules) Formal modeling & Database Instantiation Shop level executor Planner Physical facility Resource model Simulation (task generator) Scheduler Associated with system developmentAssociated 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 Language Knowledge

System Description (extraction) Natural Language Graphical Formalism Dialog Monitor Resource Model Process Model Resource Model Execution Model User Detailed Description

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) Equipment Controllers SL 20 VF 0E ABB 2400 Puma Man MT Kardex Task Output Queue Task Output Queue Databas e Scheduler Task Input Queue Task Input Queue ABB 140

Simulation-based Scheduling Dynamic Link Library Remote Procedure Call Database Statistical Analysis Best Rule Selection ARENA: Real-time "fastmode.bat" file ARENA: fast-mode Visual Basic Application Rule 1 Simulation Rule 1 Simulation Rule n Simulation Rule n Simulation Process plans Look-ahead Manager Operating policy Operating policy Order Details Order 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 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 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 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 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 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, 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, 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 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,

References Son, Young-Jun and Wysk, R. A., “Automatic simulation model generation for simulation-based, real-time control”, Computers in Industry, vol. 45, pp , 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, 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 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, 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, 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 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 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 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 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 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 References