EURO 2024 Copenhagen
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2303. New insights in hub location models

Invited abstract in session TB-26: Advanced Topics in Combinatorial Optimization, stream Combinatorial Optimization.

Tuesday, 10:30-12:00
Room: 012 (building: 208)

Authors (first author is the speaker)

1. Mercedes Landete
Departamento de Estadística y Matemática Aplicada, University Miguel Hernández of Elche
2. Juan Manuel Muñoz-Ocaña
Estadística e IO, University of Cadiz
3. Juanjo Peiró
Estadística i Investigació Operativa, Universitat de València
4. Antonio Manuel Rodriguez-Chia
Estadistica e IO, Universidad de Cádiz
5. Francisco Saldanha-da-Gama
Sheffield University Management School

Abstract

In this work we present different advances in exact techniques for solving hub location problems. Firstly, we carry out the analysis of the classical model with path variables for multiple-allocation and we propose a new family of clique constraint. Some properties of the new inequlities and corresponding lifting are discussed, and a separation heuristic algorithm is proposed. A set of computational experiments are reported to evaluate the usefulness of the proposals when embedded in a commercial solver.The new family of inequalities is notably effective in r-allocation problems. Secondly, we propose a model for the single-allocation hub location problem with upgraded connections. In this case, the goal is to invest the budget both in reducing the cost of the network and in improving some of its connections. For this new model we propose an exact resolution algorithm based on the ordered median problem and on an existing approximation for large class of binary quadratic programs. In the computational analysis we compare the results for complete and incomplete hub networks. Finally, an analysis of the added-value of upgrading is conducted.

Keywords

Status: accepted

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