Time consistent expected mean-variance in multistage stochastic quadratic optimization: a model and a matheuristic

Unai Aldasoro (), María Merino and Gloria Pérez
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Unai Aldasoro: University of the Basque Country (UPV/EHU)
María Merino: University of the Basque Country (UPV/EHU)
Gloria Pérez: University of the Basque Country (UPV/EHU)

Annals of Operations Research, 2019, vol. 280, issue 1, No 8, 187 pages

Abstract: Abstract In this paper, we present a multistage time consistent Expected Conditional Risk Measure for minimizing a linear combination of the expected mean and the expected variance, so-called Expected Mean-Variance. The model is formulated as a multistage stochastic mixed-integer quadratic programming problem combining risk-sensitive cost and scenario analysis approaches. The proposed problem is solved by a matheuristic based on the Branch-and-Fix Coordination method. The multistage scenario cluster primal decomposition framework is extended to deal with large-scale quadratic optimization by means of stage-wise reformulation techniques. A specific case study in risk-sensitive production planning is used to illustrate that a remarkable decrease in the expected variance (risk cost) is obtained. A competitive behavior on the part of our methodology in terms of solution quality and computation time is shown when comparing with plain use of CPLEX in 150 benchmark instances, ranging up to 711,845 constraints and 193,000 binary variables.

Keywords: Branch-and-Fix Coordination; Expected Conditional Risk Measures; Matheuristic algorithms; McCormick relaxation; Multistage stochastic optimization; Quadratic mixed 0–1 programming; Production planning; Time consistent risk aversion; 90C15; 90C20; 90C59 (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s10479-018-3032-7

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