Constraint generation for risk averse two-stage stochastic programs

作     者：Minguez, R. van Ackooij, W. Garcia-Bertrand, R. 

作者机构：ESCISSION Co Ciudad Real Spain EDF Elect France R&D 7 Blvd Gaspard Monge F-91120 Palaiseau France Univ Castilla La Mancha Dept Elect Engn Ciudad Real Spain 

出 版 物：《EUROPEAN JOURNAL OF OPERATIONAL RESEARCH》 (欧洲运筹学杂志)

年 卷 期：2021年第288卷第1期

页      面：194-206页

核心收录：

学科分类：1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 070104[理学-应用数学] 0701[理学-数学] 

基　　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] 

主　　题：Stochastic programming Decision analysis under uncertainty CVaR Risk aversion 

摘      要：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.