A second-order cone programming approach for linear programs with joint probabilistic constraints

Jianqiang Cheng, Abdel Lisser

Research output: Contribution to journalArticlepeer-review

45 Scopus citations

Abstract

This paper deals with a special case of Linear Programs with joint Probabilistic Constraints (LPPC) with normally distributed coefficients and independent matrix vector rows. Through the piecewise linear approximation and the piecewise tangent approximation, we approximate the stochastic linear programs with two second-order cone programming (SOCP for short) problems. Furthermore, the optimal values of the two SOCP problems are a lower and upper bound of the original problem respectively. Finally, numerical experiments are given on randomly generated data.

Original languageEnglish (US)
Pages (from-to)325-328
Number of pages4
JournalOperations Research Letters
Volume40
Issue number5
DOIs
StatePublished - Sep 2012
Externally publishedYes

Keywords

  • Joint probabilistic constraints
  • Piecewise linear approximation
  • Second-order cone programming
  • Stochastic programming

ASJC Scopus subject areas

  • Software
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering
  • Applied Mathematics

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