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Simulation Modeling

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Submitted By blr2016
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Brooklyn Warren
Chapter 10 Simulation Modeling

What is Simulation? * To try to duplicate the features, appearance, and characteristics of a real system. * Imitate a real-world situation mathematically. * Study its properties and operating characteristics. * Draw conclusions and make action decisions based on the results.

Processes of Simulation: 1. Define Problem 2. Introduce Important Variables 3. Construct Simulation Model 4. Specify Values of Variables to Be Tested 5. Conduct the Simulation 6. Examine the Results 7. Select Best Course of Action

Advantages of Simulation: * Straightforward and flexible. * Can handle large and complex systems. * Allows "what-if" questions. * Does not interfere with real-world systems. * Study interactions among variable. * "Time comparison" is possible. * Handles complications that other methods can't.

Disadvantages of Simulation: * Can be expensive and time consuming. * Does not generate optimal solutions. * Managers must generate all conditions and constraints. * Each model is unique.

Monte Carlo Simulation:
Can be used with variables that are probabilistic.
Steps:
* Establish the probability distribution for each random variable. * Use random numbers to generate random values. * Repeat for some number of replications.

Probability Distributions:
Historical data
Goodness-of-fit tests for common distributions: * Normal * Uniform * Exponential * Poisson * Binomial

Role of Computers: * Built-in random number procedures for simulating from several different probability distributions. * Easy and fast replication of a model. * Change input values and track output measures.

Simulation Software: * General purpose languages * Visual Basic, C++, Fortan, etc. * Special purpose simulation languages and programs. * GPSS, Simscript III, Visual SLAM, Extend, MicroSaint Sharp, BuildSim, ProModel, and Xcell * Less programming, easier error checking, automation. * Spreadsheet models - add-ins.

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