Simulation Optimization: A Concise Overview and Implementation Guide
Abstract
Simulation optimization (SO) is the problem of optimization in the presence of objective and constraint functions that can only be observed via a stochastic simulation. SO, owing to its flexibility, has recently grown in popularity among practitioners as a convenient formulation for optimization under uncertainty. The last two decades have also seen a parallel growth in algorithmic methodology for solving SO problems. This tutorial provides a concise guide to the state of the art for solving a few key flavors of SO. Our intended target audience is a sophisticated practitioner who is looking for algorithmic implementations (http://www.simopt.org) for solving an SO problem, or a researcher who is looking to be gently initiated into the vast SO literature. Accordingly, our discussion throughout this tutorial is kept at a very accessible level—no theorems are presented, but an attempt has been made to retain key technical details. Whenever possible, we provide pointers to stable algorithmic implementations, and good entry points into the literature.
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