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DISCRETE EVENT SIMULATION-BASED ON REAL-TIME SHOP FLOOR CONTROL

DISCRETE EVENT SIMULATION-BASED ON REAL-TIME SHOP FLOOR CONTROL. 21st European Conference on Modelling and Simulation (ECMS) June 4th - 6th, 2007, Prague / Czech Republic. Presenter: Franck FONTANILI Authors: Samieh MIRDAMADI, Franck FONTANILI and Lionel DUPONT

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DISCRETE EVENT SIMULATION-BASED ON REAL-TIME SHOP FLOOR CONTROL

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

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

  3. Orders Information Constraints Objectives Production Control Information Flow Court or middle Term Tactic • Shop order status info. • Real time data info. • Resources efficiency • WIP quantity information • … F.O. Launching • Shop order priority • Planned decision • … F.O. Reporting SFC (Order / Monitoring) Consign Actions Information feedback Real Time Operational Operation System Row material Goods a. SFC definition & its sub-functions b. A classification of SFC c. Control tools to aid decision-making d. Problematic of SFC • Shop Floor Control [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]

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

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

  6. 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. Use On-Line Discrete Events Simulation for the decision-making aid in Reactive and Proactive Control of workshop by coupling withMES a. SFC definition & Its sub-functions b. A classification of SFC c. Control tools to aid decision-making d. Problematic of SFC • Shop Floor Control [SFC]

  7. Off-Line Simulation application (No direct connection) • Control parameters preparation before execution • Application of the estimated parameters • Determinist or strong probability (distributed cycle time…) • What-If  ? • Off-Line Simulation application (No direct connection) • Control parameters optimization before execution • Very weak probability (breakdown, urgent orders…) Monitoring Simulation Filtering Measure • What- Now ? • On-Line Simulation application (direct connection) • Control parameters correction (during execution) • Minimize the variations compared to the objective Identification Simulation Decision Simulation Evaluation Validation Optimization Optimization Validation Correction a. DES Application b. Off-Line Simulation process c. On-Line Simulation process • DES-based on SFC Predictive Control Proactive Control Reactive Control

  8. Off-Line Simulation application (No direct connection) • Control parameters preparation before execution • Application of the estimated parameters • Determinist or strong probability(distributed cycle time) • What-If  ? • Off-Line Simulation application (No direct connection) • Control parameters optimization before execution • Very weak probability (breakdown, urgent orders…) Shop orders To realize • What- Now ? • On-Line Simulation application (direct connection) • Control parameters correction (during execution) • Minimize the variations compared to the objective Off-Line Simulation Estimate the most frequent disturbances Diverse scenarios of Off-Line Simulation Prepare the relevant solutions No Achieved objective? ? Results analysis Yes Case Base Results of Proactive Simulations Application yes Validation Achieved objective? Shop orders execution In progress Classification of the unforeseen events Monitoring of the real system state Known events? No No Filtering the events to release simulator Scenarios of Real-time Simulation The current state of the system No Events release ? Optimization of the Control parameters Correction of the control parameters on the model Yes Initialization of the simulation model yes Variation on objective? Activate the On-Line Simulation No Realizable decision-making in real time Validation (Without modification) Achieved objective? Yes Correction of the control parameters on the real system No a. DES Application b. Off-Line Simulation process c. On-Line Simulation process • DES-based on SFC Predictive Control Proactive Control Reactive Control

  9. Predictive Control Process Off-Line Simulation Measure Decision Validation Reactive Control Process Monitoring Filtering On-Line Simulation Evaluation Correction Optimization Proactive Control Process Identification Off-Line Simulation Optimization Validation Stocking a. DES Application b. Off-Line Simulation process c. On-Line Simulation process • DES-based on SFC

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

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

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

  13. MES for SFC a. Positioning in I.S b. Aid for Real-time control Information Decision • 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. APS Strategic Management I.S. Differed Time ERP MES Tactic Supervision Real Time Order / Monitoring Fabrication I.S. Operational Fabrication process

  14. MES for SFC a. Positioning in I.S b. Aid for Real-time control • State and localization of the shop orders • Consumption of the materials, inventory level... • Progress compared to the planned plan • Detailed history of the product evolution • Follow-up of the drifts and detected anomalies • Sorting, filters and analyses • Releases of alarms by actuators • Direct access to the recorded data by D.B. The MES systems ensure the reactive control of the workshops of production

  15. Requirements evolvement into On-Line Simulation I. Validation of the simulation model The validation is the process which allows to determine in which measure a model or a simulation are a faithful representation of the real world. [DMSO, 97] Why is the validation important? • 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

  16. Requirements evolvement into On-Line Simulation II. On-Line connection MES Simulation Model OPC Data Base Supervision Ethernet networks of the real system API API API … III. Data usable: Availability and Correctness of the data Data usable Availability Correctness complete Incomplete Errors Up to dateness  Way to collect Measure Application

  17. 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 V. Classification and events analyzes Events Unforeseen Foreseen On-Line appearances Known Unknown Unknown probability Strong Probability with lapse of time Weak Probability

  18. Requirements evolvement into On-Line Simulation 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. VII. Response time (Speed of the simulator) • Expiry of one period of control • Reasonable response time between decision-making and control execution • T depends on the characteristics of the workshop IX. Correction of the parameters in real time • To transfer and execute the best realizable solution from simulation towards MES

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

  20. 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

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