Empirical stochastic branch-and-bound for optimization via simulation

Wendy Lu Xu, Barry L. Nelson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

We introduce a new method for discrete-decision-variable optimization via simulation that combines the stochastic branch-and-bound method and the nested partitions method in the sense that we take advantage of the partitioning structure of stochastic branch and bound, but estimate the bounds based on the performance of sampled solutions as the nested partitions method does. Our Empirical Stochastic Branch-and-Bound algorithm also uses improvement bounds to guide solution sampling for better performance.

Original languageEnglish (US)
Title of host publicationProceedings of the 2010 Winter Simulation Conference, WSC'10
Pages983-994
Number of pages12
DOIs
StatePublished - Dec 1 2010
Event2010 43rd Winter Simulation Conference, WSC'10 - Baltimore, MD, United States
Duration: Dec 5 2010Dec 8 2010

Publication series

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

Other

Other2010 43rd Winter Simulation Conference, WSC'10
CountryUnited States
CityBaltimore, MD
Period12/5/1012/8/10

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Science Applications

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