Empirical stochastic branch-and-bound for optimization via simulation

Wendy Lu Xu*, Barry L. Nelson

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


This article introduces a new method for discrete decision variable optimization via simulation that combines the nested partitions method and the stochastic branch-and-bound method in the sense that advantage is taken of the partitioning structure of stochastic branch-and-bound, but the bounds are estimated based on the performance of sampled solutions, similar to the nested partitions method. The proposed Empirical Stochastic Branch-and-Bound (ESB&B) algorithm also uses improvement bounds to guide solution sampling for better performance. A convergence proof and empirical evaluation are provided.

Original languageEnglish (US)
Pages (from-to)685-698
Number of pages14
JournalIIE Transactions (Institute of Industrial Engineers)
Issue number7
StatePublished - 2013


  • Optimization via simulation
  • Stochastic branch-and-bound

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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