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Operations Management
William J. Stevenson 8th edition
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CHAPTER 18s Simulation Operations Management, Eighth Edition, by William J. Stevenson Copyright © 2005 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin
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Simulation Simulation: a descriptive technique that enables a decision maker to evaluate the behavior of a model under various conditions. Simulation models complex situations Models are simple to use and understand Models can play “what if” experiments Extensive software packages available
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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
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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
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Example S-1
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Example S-1
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Simulating Distributions
Poisson Mean of distribution is required Normal Need to know the mean and standard deviation Simulated value Mean Random number Standard deviation + X =
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Uniform Distribution = Figure 18S.1 F(x) a b x Simulated value
a b x Simulated value a + (b - a)(Random number as a percentage) =
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Negative Exponential Distribution
Figure 18S.2 F(t) T t
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Computer Simulation Simulation languages SIMSCRIPT II.5 GPSS/H GPSS/PC
RESQ
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Advantages of Simulation
Solves problems that are difficult or impossible to solve mathematically Allows experimentation without risk to actual system Compresses time to show long-term effects Serves as training tool for decision makers
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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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CHAPTER 18s Additional PowerPoint slides contributed by Geoff Willis, University of Central Oklahoma.
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Why Simulate? Math too complicated Easier to manipulate than reality
Software and hardware permit modeling
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Simulation Steps Problem formulation Model building Data acquisition
Model translation Verification & validation Experiment planning & execution Analysis Implementation & documentation
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