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Simulation

Learn about the major characteristics of simulation, how it imitates reality, and how it can be used to solve complex problems. Explore different types of simulation and its advantages and disadvantages. Discover real-life applications of simulation in industries such as manufacturing and traffic management.

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Simulation

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  1. Simulation Sesi12 Dosen Pembina: DanangJunaedi IF-UTAMA

  2. Simulation • A technique for conducting experiments with a computer on a model of a management system • Frequently used DSS tool • Major Characteristics of Simulation • Simulation imitates reality and capture its richness • Simulation is a technique for conducting experiments • Simulation is a descriptive not normative tool • Simulation is often used to solve very complex, risky problems IF-UTAMA

  3. What is Simulation IF-UTAMA

  4. Simulation for Decision Makers • In DSS, simulation refers to a technique for conducting experiments with a computer on a model of a management system. • Characteristics of Simulation; • While models in general represent reality, simulation usually imitates it closely. • It is a technique for conducting experiments. • It can describe and/or predict the characteristics of a given system under different circumstances. • It can be used for complex decision making IF-UTAMA

  5. Case : Simulation Saves Siemens Millions Problem: • Siemens Solar Industries (SSI), the world’s largest maker of solar electric products, suffered continuous problems in poor material flow, unbalanced resource use, bottlenecks in throughput & schedule delays. Solution: • SSI built a cleanroom contamination-control technology. • The simulation provided a virtual laboratory for engineers to experiment with various configurations before the physical systems were constructed. Results: • SSI improved their manufacturing process significantly. • The cleanroom facility saved SSI over $75 million/ year. IF-UTAMA

  6. Advantages and Disadvantages of Simulation IF-UTAMA

  7. Limitations of Simulation • Cannot guarantee an optimal solution • Slow and costly construction process • Cannot transfer solutions and inferences to solve other problems • So easy to sell to managers, may miss analytical solutions • Software is not so user friendly IF-UTAMA

  8. Simulation Methodology • Set up a model of a real system and conduct repetitive experiments 1. Problem Definition 2. Construction of the Simulation Model 3. Testing and Validating the Model 4. Design of the Experiments 5. Conducting the Experiments 6. Evaluating the Results 7. Implementation IF-UTAMA

  9. Simulation Types • Probabilistic Simulation • Distribution: • Discrete distributions : systems monitor the systems each time a change in its state takes place • Continuous distributions : system monitor changes in a state of system at descret points in time • Probabilistic simulation via Monte Carlo technique • Time Dependent versus Time Independent Simulation • Simulation Software • Visual Simulation • Object-oriented Simulation IF-UTAMA

  10. Simulation Development IF-UTAMA

  11. Some Applications of Simulation IF-UTAMA

  12. Visual Spreadsheets • User can visualize models and formulas with influence diagrams • Not cells--symbolic elements IF-UTAMA

  13. Visual Interactive Modeling (VIM) • Visual interactive modeling (VIM), also called • Visual interactive problem solving • Visual interactive modeling • Visual interactive simulation • Use computer graphics to present the impact of different management decisions. • Can integrate with GIS • Users perform sensitivity analysis • Static or a dynamic (animation) systems IF-UTAMA

  14. Generated Image of Traffic at an Intersection from the Orca Visual Simulation Environment (Courtesy Orca Computer, Inc.) IF-UTAMA

  15. Visual Interactive Simulation (VIS) • Decision makers interact with the simulated model and watch the results over time • Visual interactive models and DSS • Queueing IF-UTAMA

  16. Monte Carlo Simulation IF-UTAMA

  17. Monte Carlo Technique IF-UTAMA

  18. Step 1 Probability Distribution IF-UTAMA

  19. Step 2 Building a Cumulative Probability Distribution IF-UTAMA

  20. Step 3 Setting Random Number Interval IF-UTAMA

  21. Step 4 Generating Random Numbers IF-UTAMA

  22. Step 5 Simulating the Experience IF-UTAMA

  23. IF-UTAMA

  24. Simulation of Queuing Problem IF-UTAMA

  25. Queuing Problem IF-UTAMA

  26. Dist 1 Inter-Arrival Times IF-UTAMA

  27. Dist 2 Unloading Times IF-UTAMA

  28. Example IF-UTAMA

  29. Example-contd : Some Simple Statistic IF-UTAMA

  30. Simulation and Inventory Analysis The Basic Model IF-UTAMA

  31. Referensi • Dr. Mourad YKHLEF,2009,Decision Support System-Simulation, King Saud University • Richard K. Min.2002.Information Systems for Management. OUR LADY OF THE LAKE UNIVERSITY SCHOOL OF BUSINESS • Insoo Hwang.-. Modeling and Analysis. Department of MIS, Jeonju university • Efraim Turban and Jay E. Aronson.2001. Decision Support Systems and Intelligent Systems 6th edition. Prentice Hall IF-UTAMA

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