Partial sample average approximation method for chance constrained problems

Jianqiang Cheng, Céline Gicquel, Abdel Lisser

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

In this paper, we present a new scheme of a sampling-based method to solve chance constrained programs. The main advantage of our approach is that the approximation problem contains only continuous variables whilst the standard sample average approximation (SAA) formulation contains binary variables. Although our approach generates new chance constraints, we show that such constraints are tractable under certain conditions. Moreover, we prove that the proposed approach has the same convergence properties as the SAA approach. Finally, numerical experiments show that the proposed approach outperforms the SAA approach on a set of tested instances.

Original languageEnglish (US)
Pages (from-to)657-672
Number of pages16
JournalOptimization Letters
Volume13
Issue number4
DOIs
StatePublished - Jun 1 2019
Externally publishedYes

Keywords

  • Chance constraints
  • Sampling-based method
  • Stochastic programming

ASJC Scopus subject areas

  • Control and Optimization

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