Decision-Theoretic Foundations of Simulation Optimization
Published Online: 15 FEB 2011
Copyright © 2010 John Wiley & Sons, Inc. All rights reserved.
Wiley Encyclopedia of Operations Research and Management Science
How to Cite
Frazier, P. I. 2011. Decision-Theoretic Foundations of Simulation Optimization. Wiley Encyclopedia of Operations Research and Management Science. .
- Published Online: 15 FEB 2011
In simulation optimization, a task appearing frequently in applications, we wish to find a set of inputs to a simulator that causes its output to be maximal in some sense. Within any simulation optimization algorithm, we must make a sequence of decisions about which input to test at each point in time. The problem of making this sequence of decisions so as to discover a near-optimal point as quickly as possible may be understood within a decision-theoretic framework. We describe this decision-theoretic framework in the context of two problems: ranking and selection and Bayesian global optimization. This decision-theoretic framework provides both a way of better understanding existing algorithms, and a path to developing new algorithms.
- bayesian global optimization;
- ranking and selection;
- bayesian statistics;
- dynamic programming