Simulation Optimization and a Case Study

Simulation Optimization and a Case Study

Banu Y. Ekren (Izmir University of Economics, Turkey), Sunderesh S. Heragu (Oklahoma State University, USA), Gerald W. Evans (University of Louisville, USA) and William E. Biles (University of Louisville, USA)
Copyright: © 2014 |Pages: 12
DOI: 10.4018/978-1-4666-5202-6.ch194
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Gradient-Based Simulation Optimization And Commercial Software Packages

Issues, Controversies, Problems

Differentiation of a function is often used to find an optimum point for that function. Although a gradient-based approach requires a mathematical expression of the objective function, when such mathematical expression cannot be obtained, there is a need to use an estimation technique to initiate the solution procedure. PA, LR, FD, and FDA methods are the four successfully used gradient estimators that we discuss in this chapter. We also discuss several simulation commercial software packages with associated optimization tools.

Key Terms in this Chapter

Perturbation Analysis: It examines the output of a model to changes in its input variables.

Optimization: It is the selection of the best value based on some criteria from some set of available alternatives.

Gradient: Gradient vector of a function is the partial derivatives with respect to each of the independent variables.

Gradient-Based Simulation Optimization: A gradient-based approach requires a mathematical expression of the objective function, when such mathematical expression cannot be obtained. Simulation based gradient estimation is a technique to start the solution procedure.

Simulation Optimization: It is the optimization of an output by finding the best input variable values from among all possibilities without explicitly evaluating each possibility of these input variable values.

OptQuest: It is an optimization tool provided in ARENA simulation software.

Input Variable: It is the variable whose values affect the output (response) of the system.

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