Mathematical Programming

, Volume 83, Issue 1, pp 425–450

A branch and bound method for stochastic global optimization

  • Vladimir I. Norkin
  • Georg Ch. Pflug
  • Andrzej Ruszczyński
Article

DOI: 10.1007/BF02680569

Cite this article as:
Norkin, V.I., Pflug, G.C. & Ruszczyński, A. Mathematical Programming (1998) 83: 425. doi:10.1007/BF02680569

Abstract

A stochastic branch and bound method for solving stochastic global optimization problems is proposed. As in the deterministic case, the feasible set is partitioned into compact subsets. To guide the partitioning process the method uses stochastic upper and lower estimates of the optimal value of the objective function in each subset. Convergence of the method is proved and random accuracy estimates derived. Methods for constructing stochastic upper and lower bounds are discussed. The theoretical considerations are illustrated with an example of a facility location problem.

Keywords

Stochastic programmingGlobal optimizationBranch and bound methodFacility location

Copyright information

© The Mathematical Programming Society, Inc 1998

Authors and Affiliations

  • Vladimir I. Norkin
    • 1
  • Georg Ch. Pflug
    • 1
  • Andrzej Ruszczyński
    • 2
  1. 1.International Institute for Applied Systems AnalysisLaxenburgAustria
  2. 2.Department of Management Science and Information SystemsRutgers UniversityPiscatawayUSA