This book thus far has focused on the optimization of industrial processes where a physical system or process exists and needs to be improved. There is a growing awareness in the Statistics and Engineering literature for the need and usefulness of methods for the optimization of models of such physical processes. The models are a surrogate of a process or product, used to study and improve it with no active intervention and faster experimentation. When optimizing a simulation model, the optimal solution obtained from the simulation is implemented in the real system. Evidently, the model must be an accurate representation of the system under study. We will not delve into the deep subject of simulation modeling and validation, for which a very large body of literature exists (see e.g., the books [83, 4, 143]) and is outside the scope of the present book. Our purpose in this chapter is to provide an introduction to some of the techniques that are useful in the optimization of simulated systems.
KeywordsStochastic Approximation Empirical Distribution Function Stochastic Gradient Simulation Optimization Repeat Part
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