Introduction, Simulation Process and Stochastic Modeling

Systems Optimization and Analysis Simulation Module Bio • Eng.D in Production Systems from Ecole des Mines de Paris, France • Ph.D in Operations Research from MIT • Research: Manufacturing Revenue Management, Dynamic Pricing, Auctions, Procurement, Scheduling… • Experience in eCommerce Fulfillment, Electronics, Aeronautics, Transportation and Software What is Simulation? • Application Domains: Science, Justice, Engineering, Education, Entertainment, Management… Experiment Input A System Model Modeling Input A Real System Outcome Input B System Model Validation Outcome Outcome Inference Input B Real System Outcome Types of Simulation • Physical Vs. Digital • Static Vs. Dynamic • Discrete Event Vs. Continuous Time This Module: Digital, Static & Dynamic Discrete Event Simulation Simulation Module Goals • Develop the practical skills necessary to design, implement and analyze discreteevent simulation systems; Practice of Modeling!!! • Cover the basic theory underlying discreteevent simulation methods. Simulation Module Outline Week 1 Class Date Topic 1 15-Jul Introduction, Simulation Process Reading Assignment ClearPictures, Inc. Questions 1 to 3 in ClearPictures, Inc. and Stochastic Modeling 2 16-Jul Monte-Carlo Theory and Proba/Stat Review 1 Checklist Examples (with Crystal Ball) TUT1 17-Jul Monte-Carlo Modeling and Homework 1 Crystal Ball Tutorial 2 3 21-Jul Ontario Gateway Case Ontario Gateway Case Write-Up Discussion 4 22-Jul Discrete-Event Framework and Introduction to Simul8 Implement ClearPictures model in Simul8 Examples (with Simul8) TUT2 24-Jul Discrete-Event Modeling and Homework 2 Simul8 Tutorial 3 5 28-Jul Discrete Event Case Human Genome Case Write-Up Discussion 6 29-Jul Experimental Design and Simulation Analysis 7 Proba/Stat Review 2 Checklist 30-Jul Advanced Modeling Examples, Simulation-Based Optimization TUT3 31-Jul Experimental Design and Simulation Analysis Tutorial Homework 3 Optional References • Law, A. and W. Kelton, Simulation Modeling and Analysis, 3rd ed., McGraw-Hill (2000). • Ross, S., Simulation, 3rd ed., Academic Press (2002). • Swain, J., “Power Tools for Visualization and Decision-Making,” OR/MS Today, February 2001. Available online at The Simulation Process 1 Define the simulation goal • Never skip! 2 Model the system • Keep the goal in mind! • Customer feedback! 3 Implement the model • Choice of tool is key 4 Debug, Validate, Sensitivity • Never skip! • Customer feedback! 5 Design the experiment • Run length, warm start, variance reduction… 6 Run the experiment 7 Analyze and communicate • Use confidence intervals! Simulation Goal System Design Vs. System Analysis Strategic? Tactical? Key Performance Measures? Control? • What about ClearPictures, Inc.? ClearPictures: Simulation Goals • Estimate the average and standard dev. of delivery leadtime through the pull section; • Estimate the average and standard dev. of WIP inventory through the pull section; • Determine the production bottleneck; • Estimate the impact of purchasing more machines on leadtime and inventory; • … System Modeling • “Everything should be made as simple as possible, but not simpler.” Albert Einstein. • The simulation goal should be the guiding light when deciding what to model • Get client/user feedback early, and maintain model + assumption sheet for communication purposes • For random variables, collect data and fit distributions… after modeling the system, with sensitivity analysis in mind! System Modeling 2 Model the system 1 Define simulation goal 2a Model on paper • Process Flow Diagram, Flow Chart… 2b User/Client feedback • Model assumption sheet! 2c Sensitivity analysis • Theoretical: TOC, Queueing theory… 2d Data collection & Fit • Prioritize, mock data example, fitting software 3 Implement the model 4 Debug, Validate, Sensitivity 5 Design the experiment ClearPictures Production Model Box / Sensor Board Assembly Sensor Firmware Test U[15,25] Inspection N[9.5,4] ST2 ST1 TRIAN[5,10,15] U[13,24] U[10,15] 15% 85% 360 Customer Model Implementation • • • • General programming language (C++, Java…) Simulation-oriented language (MODSIM…) Simulation software with GUI (Simul8®, Witness…) Excel Add-in (Crystal Ball, @Risk…) FLEXIBILITY COST REQ. SKILLS DEV. TIME RUN TIME Very High Low Very High High Low Simulation Language High High High Medium Low Simulation Software Medium High Medium Low Medium Low Low Low Lowest High General Prog. Language Excel Add-in Validation & Debugging • Slow Graphical Animation • Step-by-step event list Experiment Design • • • • Warm-up Period? Run Length? Number of Trials? How to analyze and interpret the results? Class 1 Wrap-Up 1. Simulation Process 2. Modeling 3. Choice of simulation tool