New algorithmic framework for conditional value at risk: Application to stochastic fixed-charge transportation
Justo Puerto and
European Journal of Operational Research, 2019, vol. 277, issue 1, 215-226
This paper introduces a new algorithmic scheme for two-stage stochastic mixed-integer programming assuming a risk averse decision maker. The focus is the minimization of the conditional value at risk for a hard combinatorial optimization problem. Some properties of a mixed-integer non-linear programming formulation for conditional value at risk are studied as well as their algorithmic implications. This yields to a procedure for obtaining lower and upper bounds on the optimal value of the problem that may lead to an optimal solution. The new developments are applied to a fixed-charge transportation problem with stochastic demand, and they are computationally tested. The corresponding results are thoroughly presented and discussed.
Keywords: Transportation; Stochastic mixed-integer programming; CVaR (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:277:y:2019:i:1:p:215-226
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