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Lecture

Lecture. 6. Simulation Chapter 18S. Simulation Is …. Simulation – very broad term methods and applications to imitate or mimic real systems, usually via computer Applies in many fields and industries Simulation models complex situations Models are simple to use and understand

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Lecture

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  1. Lecture 6 Simulation Chapter 18S

  2. Simulation Is … • Simulation – very broad term • methods and applications to imitate or mimic real systems, usually via computer • Applies in many fields and industries • Simulation models complex situations • Models are simple to use and understand • Models can play “what if” experiments • Extensive software packages available • ARENA, ProModel • Very popular and powerful method

  3. Examples • Manufacturing facility • Bank operation • Airport operations (passengers, security, planes, crews, baggage) • Transportation/logistics/distribution operation • Hospital facilities (emergency room, operating room, admissions) • Freeway system • Business process (insurance office) • Fast-food restaurant • Supermarket • Emergency-response system • Military

  4. A Simulation Model

  5. Electronic Assembly/Test System • Produce two different sealed elect. units (A, B) • Arriving parts: cast metal cases machined to accept the electronic parts • Part A, Part B – separate prep areas • Both go to Sealer for assembly, testing – then to Shipping (out) if OK, or else to Rework • Rework – Salvaged (and Shipped), or Scrapped

  6. Part A • Interarrivals: expo (5) minutes • From arrival point, proceed immediately to Part A Prep area • Process = (machine + deburr + clean) ~ tria (1,4,8) minutes • Go immediately to Sealer • Process = (assemble + test) ~ tria (1,3,4) min. • 91% pass, go to Shipped; Else go to Rework • Rework: (re-process + testing) ~ expo (45) • 80% pass, go to Salvaged; Else go to Scrapped

  7. Part B • Interarrivals: batches of 4, expo (30) min. • Upon arrival, batch separates into 4 individual parts • From arrival point, proceed immediately to Part B Prep area • Process = (machine + deburr +clean) ~ tria (3,5,10) • Go to Sealer • Process = (assemble + test) ~ weib (2.5, 5.3) min., different from Part A, though at same station • 91% pass, go to Shipped; Else go to Rework • Rework: (re-process + test) = expo (45) min. • 80% pass, go to Salvaged; Else go to Scrapped

  8. Run Conditions, Output • Start empty & idle, run for four 8-hour shifts (1,920 minutes) • Collect statistics for each work area on • Resource utilization • Number in queue • Time in queue • For each exit point (Shipped, Salvaged, Scrapped), collect total time in system (a.k.a. cycle time)

  9. Simulation Models Are Beneficial • Systematic approach to problem solving • Increase understanding of the problem • Enable “what if” questions • Specific objectives • Power of mathematics and statistics • Standardized format • Require users to organize

  10. Simulation Process • Identify the problem • Develop the simulation model • Test the model • Develop the experiments • Run the simulation and evaluate results • Repeat 4 and 5 until results are satisfactory

  11. Monte Carlo Simulation Monte Carlo method: Probabilistic simulation technique used when a process has a random component • Identify a probability distribution • Setup intervals of random numbers to match probability distribution • Obtain the random numbers • Interpret the results

  12. Different Kinds of Simulation • Static vs. Dynamic • Does time have a role in the model? • Continuous-change vs. Discrete-change • Can the “state” change continuously or only at discrete points in time? • Deterministic vs. Stochastic • Is everything for sure or is there uncertainty? • Most operational models: • Dynamic, Discrete-change, Stochastic

  13. Advantages of Simulation • Solves problems that are difficult or impossible to solve mathematically • Flexibility to model things as they are (even if messy and complicated) • Allows experimentation without risk to actual system • Ability to model long-term effects • Serves as training tool for decision makers

  14. Limitations of Simulation • Does not produce optimum solution • Model development may be difficult • Computer run time may be substantial • Monte Carlo simulation only applicable to random systems

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