期刊
MATHEMATICAL PROGRAMMING
卷 89, 期 1, 页码 55-77出版社
SPRINGER HEIDELBERG
DOI: 10.1007/PL00011393
关键词
probabilistic programming; discrete distributions; generalized concavity; column generation
We consider stochastic programming problems with probabilistic constraints involving integer-valued random variables. The concept of a p-efficient point of a probability distribution is used to derive various equivalent problem formulations. Next we introduce the concept of r-concave discrete probability distributions and analyse its relevance for problems under consideration. These notions are used to derive lower and upper bounds for the optimal Value of probabilistically constrained stochastic programming problems with discrete random variables. The results are illustrated with numerical examples.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据