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发布时间：2019-03-10
论文类型：期刊论文
发表时间：2013-07-01
发表刊物：IIE TRANSACTIONS
收录刊物：SSCI、EI、SCIE
卷号：45
期号：7,SI
页面范围：716-735
ISSN号：0740-817X
关键字：Joint chance-constrained program; Monte Carlo; stochastic optimization
摘要：This article studies Joint Chance-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 smooth Monte 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.