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DELS Analysis/Synthesis Methodology

DELS Analysis/Synthesis Methodology. John Fowler. And now for something completely... the same. Dagstuhl 2002 Workshop Grand Challenges in Modeling & Simulation Manufacturing Working Group. John W. Fowler Oliver Rose Steffen Strassburger Steve Turner. Our Vision.

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DELS Analysis/Synthesis Methodology

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  1. DELS Analysis/Synthesis Methodology John Fowler

  2. And now for something completely... the same

  3. Dagstuhl 2002 WorkshopGrand Challenges in Modeling & SimulationManufacturing Working Group John W. Fowler Oliver Rose Steffen Strassburger Steve Turner

  4. Our Vision Pervasive Use of Simulation for Decision Support in Manufacturing

  5. Grandest Challenge #1 • An order of magnitude reduction in problem solving cycles • Problem Definition • Data/Information Collection** • Proactive Data Analysis, Better Factory Discipline • Model Generation with appropriate complexity* • Experimentation** • Reducing Model Complexity, VRT’s, Parallelism • Analysis of Output including Validation* • Implementation of Results ** Very time consuming * Time consuming

  6. Grand Challenge #2 • Development of real-time simulation-based problem solving capability • Factory status constantly changes – often abruptly • Need for quick what-if analysis on demand • Current capability limited by: • Data / Information collection • Experimentation • May be desirable to have persistent model constantly updated from manufacturing system.

  7. Grand Challenge #3 • True Plug-and-Play Interoperability of Simulations and Supporting Software within a Specific Application Domain • Supporting Software includes: • Manufacturing Execution Systems, • Available to Promise Systems, • Analysis Software, … • HLA is a partial solution

  8. Grand Challenge #4 • Efficient Hierarchical Simulations of Manufacturing Systems/Supply Chains • Levels: • Machine Level (workcell) • Factory Level • Enterprise/Supply Chain Level • Level of Abstraction? • Parallel/Distributed? • How should information be shared between the levels?

  9. Big Challenge #5 • Greater Acceptance of Modeling & Simulation within Industry • Slowly gaining acceptance for some applications e.g. capacity planning • Too much time still spent justifying the use of models • Need to manage expectations • Must resist the temptation to oversell potential model results • Tools for maintaining simulation models of factories are resource intensive

  10. Additional Needs • Fast evaluation of new algorithms • Enhanced simulation for short-term planning • Combined optimization and simulation • Robust solutions • Multi-objective solutions • More optimal/most optimal solutions ;-) • Explicit incorporation of risk • Plug and Play use of solution techniques • Easy hook up of modeling/solution techniques

  11. Multi-Product Cycle Time and Throughput Evaluation via Simulation on Demand Arizona State University1: Dr. John W. Fowler Dr. Gerald T. Mackulak Northwestern University2: Dr. Barry L. Nelson Dr. Bruce E. Ankenman 1 SRC Task 1224.001 Department of Industrial Engineering, Arizona State University 2 SRC Task 1225.001 Department of Industrial Engineering and Management Sciences, Northwestern University Technical Thrust: Factory Systems

  12. “Simulation on Demand?” • For years we have worried about how to make simulations faster or more statistically efficient • In our context (and many others), simulation time is plentiful, but decision-maker time is scarce • The philosophy of “simulation on demand” is to use intensive simulation up front to build a model structure that is independent of the specific queries that might be needed to support decision making

  13. Simulated Average CT Estimates over the Product Group Start Rates Products 3,4,6,7 Products 1,2,5 [Times in hr, Rates in Wafers/hr]

  14. Simulation on Demand • Emphasis is on the efficiency of obtaining useful simulation results, rather than on the efficiency of the simulation run itself • cRSM represents a bridge between the flexibility of simulation and the insight provided by an analytical queueing model by delivering simulation results on demand

  15. Questions We Want to Answer On Demand • What is the weighted cycle time of the factory at a particular throughput and product mix? • What is the 80th percentile of cycle time for products at a particular throughput and product mix? • What are the feasible values of throughput and product mix such that average cycle-time constraints are met? • What is the impact on the cycle times of products 1,2,…, k-1 of increasing the throughput of product k to meet increased demand? • What product mix maximizes revenue while keeping cycle times below required limits?

  16. Project Overview

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