A smooth Monte Carlo approach to joint chance-constrained programs

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)716-735
Journal / PublicationIIE Transactions (Institute of Industrial Engineers)
Volume45
Issue number7
StatePublished - 2015
Externally publishedYes

Abstract

This article studies JointChance-Constrained Programs (JCCPs). JCCPs are often non-convex and non-smooth and thus are generally challenging to solve. This article proposes a logarithm-sum-exponential smoothing technique to approximate a joint chance constraint by the difference of two smooth convex functions, and uses a sequential convex approximation algorithm, coupled with a Monte Carlo method, to solve the approximation. This approach is called a smoothMonte Carlo approach in this article. It is shown that the proposed approach is capable of handling both smooth and non-smooth JCCPs where the random variables can be either continuous, discrete, or mixed. The numerical experiments further confirm these findings.

Research Area(s)

  • Joint chance-constrained program, Monte Carlo, Stochastic optimization