Deriving a new lower bound in stochastic programming problem considering variance

Takayuki Shiina, Yu Tagaya, Susumu Morito, Jun Imaizumi

Research output: Contribution to journalArticle

Abstract

One approach in stochastic programming involves minimizing the expectation of cost, which includes a recourse function to represent the penalties for constraint violation. A known solution for such problems uses the L-shaped method based on the Benders decomposition. Stochastic programming problems that take the variance of the recourse function into account are non-convex problems, and an exact solution based on the branch-and-bound method has previously been demonstrated. However, since there are cases where the lower bound for the variance of the recourse function used in the branch-and-bound method is not effective, we present a new lower bound and demonstrate the effectiveness of the resulting solution by a numerical experiment.

Original languageEnglish
Pages (from-to)1851-1856
Number of pages6
JournalICIC Express Letters
Volume8
Issue number7
Publication statusPublished - 2014 Jul
Externally publishedYes

Keywords

  • Branch-and-bound
  • Lower bound
  • Stochastic programming

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)

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