Discrete optimization via simulation

L. Jeff Hong, Barry L Nelson, Jie Xu*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

16 Scopus citations

Abstract

This chapter describes tools and techniques that are useful for optimization via simulation—maximizing or minimizing the expected value of a performance measure of a stochastic simulation—when the decision variables are discrete. Ranking and selection, globally and locally convergent random search and ordinal optimization are covered, along with a collection of “enhancements” that may be applied to many different discrete optimization via simulation algorithms. We also provide strategies for using commercial solvers.

Original languageEnglish (US)
Title of host publicationInternational Series in Operations Research and Management Science
PublisherSpringer New York LLC
Pages9-44
Number of pages36
DOIs
StatePublished - Jan 1 2015

Publication series

NameInternational Series in Operations Research and Management Science
Volume216
ISSN (Print)0884-8289

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Strategy and Management
  • Management Science and Operations Research
  • Applied Mathematics

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  • Cite this

    Hong, L. J., Nelson, B. L., & Xu, J. (2015). Discrete optimization via simulation. In International Series in Operations Research and Management Science (pp. 9-44). (International Series in Operations Research and Management Science; Vol. 216). Springer New York LLC. https://doi.org/10.1007/978-1-4939-1384-8_2