L. F. Escudero

This work focuses on a stochastic mixed-integer linear optimization modeling framework and a matheuristic approach for solving the multistage capacitated allocation hub location network expansion planning under uncertainty. The strategic decisions are the hub location in a network and their initial capacity dimensioning as well as its expansion along a time horizon. Two types of uncertain parameters are considered namely, strategic and operational ones. The strategic uncertainty is stagewise-dependent. The operational uncertainty is stage-dependent, both being captured by a finite set of scenarios. Given the dimensions of the instances in real-life applications (due to the large-scale hub network dimensions and the cardinality of the joint strategic multistage operational two-stage scenario trees to properly represent the inherent uncertainty, it is unrealistic to seek the optimal solution. So, a sort of matheuristics should be looked for. The so-named SFR3 matheuristic decomposition algorithm is introduced for Scenario variables Fixing and constraints and binary variables' integrality iteratively Randomized Relaxation Reduction, where several strategies are considered. The performance of the overall approach is computationally assessed by using stochastic-based perturbed well-known CAB data.

Keywords: hub network location, stochastic optimization, multistage network expansion planning, strategic and tactical uncertainties, fix-and-randomized-relaxation-reduction matheuristic.


FE1-P3 Plenary. On dynamic multiple allocation capacitated hub location expansion planning under uncertainty
June 11, 2021  4:15 PM
1 - GB Dantzig

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