INTRODUCTION
Optimization in operations research and the management sciences is generally identified with mathematical programming techniques, where analytical expressions for quantities of interest are available. The context of simulation optimization is a stochastic setting that defies analytical tractability, necessitating the use of simulation for estimating (through statistical sampling) system performance measures. The usual generic form of the problem is given as
where θ is the controllable set of parameters and Θ defines the constraint set on θ. We will assume throughout that the objective function is an expectation, that is,
with ω representing a sample path (or simulation replication) and L(θ, ω) the corresponding sample performance estimate. Objective functions of other forms (e.g., quantiles) can also be handled in a similar manner, and of course probabilities are simply expectations of indicator functions. In contrast to mathematical programming objective functions,...
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Fu, M.C. (2001). SIMULATION Optimization . In: Gass, S.I., Harris, C.M. (eds) Encyclopedia of Operations Research and Management Science. Springer, New York, NY. https://doi.org/10.1007/1-4020-0611-X_958
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DOI: https://doi.org/10.1007/1-4020-0611-X_958
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