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
Externally publishedYes

Fingerprint

Stochastic programming
Branch and bound method
Decomposition
Costs
Experiments

Keywords

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

ASJC Scopus subject areas

  • Computer Science(all)
  • Control and Systems Engineering

Cite this

Deriving a new lower bound in stochastic programming problem considering variance. / Shiina, Takayuki; Tagaya, Yu; Morito, Susumu; Imaizumi, Jun.

In: ICIC Express Letters, Vol. 8, No. 7, 2014, p. 1851-1856.

Research output: Contribution to journalArticle

Shiina, T, Tagaya, Y, Morito, S & Imaizumi, J 2014, 'Deriving a new lower bound in stochastic programming problem considering variance', ICIC Express Letters, vol. 8, no. 7, pp. 1851-1856.
Shiina, Takayuki ; Tagaya, Yu ; Morito, Susumu ; Imaizumi, Jun. / Deriving a new lower bound in stochastic programming problem considering variance. In: ICIC Express Letters. 2014 ; Vol. 8, No. 7. pp. 1851-1856.
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