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Simulation optimization via promising region search and surrogate model approximation

  • Stony Brook University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

We propose a random search algorithm for solving simulation optimization problems with continuous decision variables. The algorithm combines ideas from promising area search, shrinking ball methods, and surrogate model optimization. We discuss the local convergence property of the algorithm and provide numerical examples to illustrate its performance.

Original languageEnglish
Title of host publication2016 Winter Simulation Conference
Subtitle of host publicationSimulating Complex Service Systems, WSC 2016
EditorsTheresa M. Roeder, Peter I. Frazier, Robert Szechtman, Enlu Zhou
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages649-658
Number of pages10
ISBN (Electronic)9781509044863
DOIs
StatePublished - Jul 2 2016
Event2016 Winter Simulation Conference, WSC 2016 - Arlington, United States
Duration: Dec 11 2016Dec 14 2016

Publication series

NameProceedings - Winter Simulation Conference
Volume0
ISSN (Print)0891-7736

Conference

Conference2016 Winter Simulation Conference, WSC 2016
Country/TerritoryUnited States
CityArlington
Period12/11/1612/14/16

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