Discrete optimization via simulation

L. Jeff Hong; Barry L. Nelson; Jie Xu

doi:10.1007/978-1-4939-1384-8_2

Discrete optimization via simulation

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

Research output: Chapter in Book/Report/Conference proceeding › Chapter

46 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 language	English (US)
Title of host publication	International Series in Operations Research and Management Science
Publisher	Springer New York LLC
Pages	9-44
Number of pages	36
DOIs	https://doi.org/10.1007/978-1-4939-1384-8_2
State	Published - 2015
Externally published	Yes

Publication series

Name	International Series in Operations Research and Management Science
Volume	216
ISSN (Print)	0884-8289

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Publisher Copyright:
© Springer Science+Business Media New York 2015.

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10.1007/978-1-4939-1384-8_2

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title = "Discrete optimization via simulation",

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.",

author = "Hong, {L. Jeff} and Nelson, {Barry L.} and Jie Xu",

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AB - 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.

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M3 - Chapter

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BT - International Series in Operations Research and Management Science

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ER -

Discrete optimization via simulation

Abstract

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