期刊
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
卷 288, 期 1, 页码 194-206出版社
ELSEVIER
DOI: 10.1016/j.ejor.2020.05.064
关键词
Stochastic programming; Decision analysis under uncertainty; CVaR; Risk aversion
资金
- Public State Employment Service of the Ministry of Labour, Migration and Social Security of Spain
- Ministry of Science, Innovation, and Universities of Spain [RTI2018-096108-A-I00, RTI2018-098703-B-I0 0]
Many practical stochastic optimization problems can be formulated as two-stage stochastic programs, and in situations where risk aversion is of interest, methods such as robust optimization or other risk-functional approaches can be used. This paper focuses on the latter case, particularly when there is a large number of scenarios, proposing a constraint generation algorithm for computational efficiency. The convergence of these algorithms is established and their effectiveness is demonstrated through various numerical experiments.
A significant share of stochastic optimization problems in practice can be cast as two-stage stochastic programs. If uncertainty is available through a finite set of scenarios, which frequently occurs, and we are interested in accounting for risk aversion, the expectation in the recourse cost can be replaced with a worst-case function (i.e., robust optimization) or another risk-functional, such as conditional value-at-risk. In this paper we are interested in the latter situation especially when the number of scenarios is large. For computational efficiency we suggest a (clustering and) constraint generation algorithm. We establish convergence of these two algorithms and demonstrate their effectiveness through various numerical experiments. (C) 2020 Elsevier B.V. All rights reserved.
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