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New algorithmic framework for conditional value at risk: Application to stochastic fixed-charge transportation

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  • Fernández, Elena
  • Hinojosa, Yolanda
  • Puerto, Justo
  • Saldanha-da-Gama, Francisco

Abstract

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.

Suggested Citation

  • Fernández, Elena & Hinojosa, Yolanda & Puerto, Justo & Saldanha-da-Gama, Francisco, 2019. "New algorithmic framework for conditional value at risk: Application to stochastic fixed-charge transportation," European Journal of Operational Research, Elsevier, vol. 277(1), pages 215-226.
    • Handle: RePEc:eee:ejores:v:277:y:2019:i:1:p:215-226
    DOI: 10.1016/j.ejor.2019.02.010
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