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Constraint generation for risk averse two-stage stochastic programs

R. Mínguez, W. van Ackooij and R. García-Bertrand

European Journal of Operational Research, 2021, vol. 288, issue 1, 194-206

Abstract: 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.

Keywords: Stochastic programming; Decision analysis under uncertainty; CVaR; Risk aversion, (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:288:y:2021:i:1:p:194-206

DOI: 10.1016/j.ejor.2020.05.064

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European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati

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