Journal
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
Volume 288, Issue 1, Pages 194-206Publisher
ELSEVIER
DOI: 10.1016/j.ejor.2020.05.064
Keywords
Stochastic programming; Decision analysis under uncertainty; CVaR; Risk aversion
Funding
- 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]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available