1. 1 DISCRETE EVENT SIMULATION-BASED ON REAL-TIME SHOP FLOOR CONTROL Presenter: Franck FONTANILI Authors: Samieh MIRDAMADI, Franck FONTANILI and Lionel DUPONT Department of Industrial Engineering/Ecole des Mines d’Albi-Carmaux/France 21st European Conference on Modelling and Simulation (ECMS) June 4th - 6th, 2007, Prague / Czech Republic

2. 2 Summary 1.Shop Floor Control [SFC] 2.Discrete Event Simulation [DES]-based on SFC 3.Manufacturing Execution System [MES] for SFC 4.Requirements evolvement into on-line simulation 5.Experimentation Plate-Form and Out-Line

3. 3 Production Control Operation System Goods Orders Information Constraints Objectives Information Flow Real Time Operational Court or middle Term Tactic Row material 1.Shop Floor Control [SFC] a.SFC definition & its sub-functions b.A classification of SFC c.Control tools to aid decision-making d.Problematic of SFC All the activities of short-term production in agreement with the objectives established by the production control, by adapting the production to the disturbances which can occur on the level of the workshop. [APICS,05], [Grabot,97] Shop order status info. Real time data info. Resources efficiency WIP quantity information … Shop order priority Planned decision … Information feedback Consign Actions F.O. Reporting F.O. Launching SFC (Order / Monitoring) SFC (Order / Monitoring)

4. 4 Exploitation Temporal axis Existing System Before Execution Off-Line ControlOn-Line Control Predictive Control To Preparation Estimated data Proactive Control To Anticipation Very weak probability of events occurrence 1.Shop Floor Control [SFC] a.SFC definition & Its sub-functions b.A classification of SFC c.Control tools to aid decision-making d.Problematic of SFC Experience feedback System in the course of execution Unforeseen events and critical drift of system variables (e.g. cycle time) Reactive Control To Correction

5. 5 ConceptionRe-engineeringExploitation Product life cycle MES Manufacturing Execution System (often coupled with the supervision) MES Manufacturing Execution System (often coupled with the supervision) CAD, CAM, CAE (DESIGN, MANUFACTURING, ENGINEERING) CAPE (PRODUCTION ENGINEERING) Specific to the Production Systems CAPM, ERP Scheduling MIS, MES, CIM, CAIT Supervision Order / Monitoring (API) 1.Shop Floor Control [SFC] a.SFC definition & Its sub-functions b.A classification of SFC c.Control tools to aid decision-making d.Problematic of SFC DES Tools for Discrete Events Simulation of flows DES Tools for Discrete Events Simulation of flows To DesignTo ImproveTo Control

6. 6 1.Shop Floor Control [SFC] a.SFC definition & Its sub-functions b.A classification of SFC c.Control tools to aid decision-making d.Problematic of SFC Use On-Line Discrete Events Simulation for the decision-making aid in Reactive and Proactive Control of workshop by coupling with MES Use On-Line Discrete Events Simulation for the decision-making aid in Reactive and Proactive Control of workshop by coupling with MES DES Allows to anticipate in the future but does not allow to direct connection to a real system in the course of execution DES Allows to anticipate in the future but does not allow to direct connection to a real system in the course of execution MES Brings a lot of information allowing to make decisions but does not allow to make sure that they are the good decisions. MES Brings a lot of information allowing to make decisions but does not allow to make sure that they are the good decisions.

7. 7 2.DES-based on SFC Proactive Control Proactive Control E.g. Machine breakdown (unforeseen but known)… Very weak probability of occurrence Off-Line Simulation Application (non direct connection) E.g. Machine breakdown (unforeseen but known)… Very weak probability of occurrence Off-Line Simulation Application (non direct connection) Reactive Control Reactive Control E.g. Cycle time Drifts of a machine (unforeseen and unknown)… To minimize the drifts compared to the deadlines envisaged On-Line Simulation Application (direct connection) E.g. Cycle time Drifts of a machine (unforeseen and unknown)… To minimize the drifts compared to the deadlines envisaged On-Line Simulation Application (direct connection) Predictive Control Predictive Control E.g.. Scheduling, to manage the capacity of the queues… Determinist or strong probability of appearance (cycle time…) Off-Line Simulation Application (non direct connection) E.g.. Scheduling, to manage the capacity of the queues… Determinist or strong probability of appearance (cycle time…) Off-Line Simulation Application (non direct connection) Objectives a. SED Application in production b. Different use of simulation

8. 8 2.DES-based on SFC Off-Line Simulation MeasureDecisionValidation Predictive Control Process Reactive Control Process MonitoringFilteringEvaluation Identification Proactive Control Process Off-Line Simulation OptimizationValidationStocking OptimizationCorrection On-Line Simulation a. SED Application in production b. Different use of simulation

9. 9 Fabrication I.S. Management I.S. Information APS ERP MES Supervision Order / Monitoring Strategic Tactic Operational Decision Real Time Differed Time Fabrication process  Executes the commands (orders) of the production control.  Delivers relevant information on the follow-up and the realization of the shop orders in real time.  Executes the commands (orders) of the production control.  Delivers relevant information on the follow-up and the realization of the shop orders in real time. 3.MES for real time Shop Floor Control

10. 10 4.Requirements evolvement into On-Line Simulation I. Validation of the simulation model II. On-Line connection III. Data usable: IV. Availability and Correctness of the data V. Data Acquisition VI. Classification and events analyzes VII. Initialization of the model VIII. Response time (Speed of the simulator) IX. Correction of the parameters in real time I. Validation of the simulation model II. On-Line connection III. Data usable: IV. Availability and Correctness of the data V. Data Acquisition VI. Classification and events analyzes VII. Initialization of the model VIII. Response time (Speed of the simulator) IX. Correction of the parameters in real time

11. 11  Connect the model to the expected or received reality  Relevance of the model: sufficient quality  Completed of the model: all the necessary information  Establish the fidelity of the simulation model  Produce a definitive proof to support the model  Reduce the risk I. Validation of the simulation model 4.Requirements evolvement into On-Line Simulation II. On-Line connection API … Data Base MES Simulation ModelOPCSupervision Ethernet networks of the real system API

12. 12 Data usable AvailabilityCorrectness completeIncomplete Up to dateness Errors Measure Application Way to collect III. Data usable: Availability and Correctness of the data 4.Requirements evolvement into On-Line Simulation IV. Data Acquisition  Use of a data base of workshop  Methods of Acquisition: Sensors: measure by Detector DBMS: Information system

13. 13 4.Requirements evolvement into On-Line Simulation V. Classification and events analyzes Events Foreseen Unforeseen Known Unknown Strong Probability with lapse of time Unknown probabilityWeak Probability On-Line appearances VI. Initialization of the model The real system must be planned from the current state of the system. To start with, a “not-empty” state of the model corresponding at the real state.

14. 14 VII. Response time (Speed of the simulator) Reasonable response time between decision-making and control execution t depends on the characteristics of the workshop VIII. Correction of the parameters in real time To transfer and execute the best realizable solution from simulation towards MES 4.Requirements evolvement into On-Line Simulation Simulation time t 1 + t t1t1 Real Time t

15. 15 Operational System Actuators Networks Sensor Ethernet networks of the real system Information System OPC Server Simulation Model (SIMBA) OPC Client Real Time Data Histories data 5.Experimentation Plate-Form and Out-Line Loading API Unloading API Operation API Transfer API D.B. M.E.S

16. 16 5.Experimentation Plate-Form and Out-Line  Experimentation of reactive control on the Technology plat-form  Collect data of the ground by MES  Filtering of the events releases  Initialization of the simulation model by injection of the collected data  Possibilities of application to a logistic chain  Performance analyze

17. 17 Thank you for your attention

18. 18 Off-Line Simulation Shop orders To realize Achieved objective? Optimization of the Control parameters yes No Validation Results analysis Predictive Control Before Execution DES-based on SFC a. Off-Line Simulation process b. On-Line Simulation process Realized Shop orders ? Reactive Control During Execution Launch of the execution of the shop orders on the real system How will it take place during the execution in case of appearance of an unforeseen event…?

19. 19 Realize the estimated parameters by predictive control Predictive Control Reactive Control During Execution Shop orders execution In progress Monitoring of the real system state Filtering the events to release simulator Events release ? Initialization of the simulation model Activate the On-Line Simulation Achieved objective? Correction of the control parameters on the model Classification of the unforeseen events Known events? Scenarios of Real-time Simulation Variation on objective? Correction of the control parameters on the real system Realizable decision-making in real time Yes No Yes Validation (Without modification) No yes No The current state of the system Case Base Application of the proactive control result Proactive Control Yes DES-based on SFC a. Off-Line Simulation process b. On-Line Simulation process

20. 20 Off-Line Simulation Shop orders To realize Achieved objective? Optimization of the Control parameters yes No Launch of the execution of the shop orders on the real system Realized Shop orders Results analysis Predictive Control Before Execution Reactive Control During Execution Estimate the most frequent disturbances Diverse scenarios of Off-Line Simulation Case Base Results of Proactive Simulations Yes No Proactive Control Before execution Prepare the relevant solutions Pooling Validation DES-based on SFC a. Off-Line Simulation process b. On-Line Simulation process