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Simulation

Chapter 14

Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall

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Chapter Topics
■ The Monte Carlo Process ■ Computer Simulation with Excel Spreadsheets

■ Simulation of a Queuing System
■ Continuous Probability Distributions

■ Statistical Analysis of Simulation Results
■ Crystal Ball ■ Verification of the Simulation Model ■ Areas of Simulation Application
Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall

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Overview
■ Analogue simulation replaces a physical system with an analogous physical system that is easier to manipulate. ■ In computer mathematical simulation a system is replaced with a mathematical model that is analyzed with the computer. ■ Simulation offers a means of analyzing very complex systems that cannot be analyzed using the other management science techniques in the text.

Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall

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Monte Carlo Process
■ A large proportion of the applications of simulations are for probabilistic models. ■ The Monte Carlo technique is defined as a technique for selecting numbers randomly from a probability distribution for use in a trial (computer run) of a simulation model. ■ The basic principle behind the process is the same as in the operation of gambling devices in casinos (such as those in Monte Carlo, Monaco).

Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall

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Monte Carlo Process Use of Random Numbers (1 of 10)


In the Monte Carlo process, values for a random variable are generated by sampling from a probability distribution. Example: ComputerWorld demand data for laptops selling for $4,300 over a period of 100 weeks.



Table 14.1 Probability Distribution of Demand for Laptop PC’s
Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall

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Monte Carlo Process Use of Random Numbers (2 of 10)


The purpose of the Monte Carlo process is to...

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