Deriving a new lower bound in stochastic programming problem considering variance

Takayuki Shiina, Yu Tagaya, Susumu Morito, Jun Imaizumi

研究成果: Article査読

抄録

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.

本文言語English
ページ(範囲)1851-1856
ページ数6
ジャーナルICIC Express Letters
8
7
出版ステータスPublished - 2014 7
外部発表はい

ASJC Scopus subject areas

  • 制御およびシステム工学
  • コンピュータ サイエンス(全般)

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