On Multistage Multiscale Stochastic Capacitated Multiple Allocation Hub Network Expansion Planning

Laureano F. Escudero and Juan F. Monge
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Laureano F. Escudero: Área de Estadística e Investigación Operativa, Universidad Rey Juan Carlos, 28933 Móstoles, Madrid, Spain
Juan F. Monge: Center of Operations Research, University Miguel Hernandez of Elche, 03202 Elche, Alicante, Spain

Mathematics, 2021, vol. 9, issue 24, 1-39

Abstract: The hub location problem (HLP) basically consists of selecting nodes from a network to act as hubs to be used for flow traffic directioning, i.e., flow collection from some origin nodes, probably transfer it to other hubs, and distributing it to destination nodes. A potential expansion on the hub building and capacitated modules increasing along a time horizon is also considered. So, uncertainty is inherent to the problem. Two types of time scaling are dealt with; specifically, a long one (viz., semesters, years), where the strategic decisions are made, and another whose timing is much shorter for the operational decisions. Thus, two types of uncertain parameters are also considered; namely, strategic and operational ones. This work focuses on the development of a stochastic mixed integer linear optimization modeling framework and a matheuristic approach for solving the multistage multiscale allocation hub location network expansion planning problem under uncertainty. Given the intrinsic difficulty of the problem and the huge dimensions of the instances (due to the network size of realistic instances as well as the cardinality of the strategic scenario tree and operational ones), it is unrealistic to seek an optimal solution. A matheuristic algorithm, so-called SFR3, is introduced, which stands for scenario variables fixing and iteratively randomizing the relaxation reduction of the constraints and variables’ integrality. It obtains a (hopefully, good) feasible solution in reasonable time and a lower bound of the optimal solution value to assess the solution quality. The performance of the overall approach is computationally assessed by using stochastic-based perturbed well-known CAB data.

Keywords: optimization under uncertainty; multistage multiscale time horizon; capacitated hub location network and expansion; strategic and tactical uncertainties; mixture of multistage and two-stage scenario trees; mixed integer linear optimization; randomized matheuristic methodology (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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