Upgrading edges in the maximal covering location problem

•The edge lengths are upgraded in the Maximal Covering Location Problem.•The optimal location of the facilities and the edge length reductions are decided.•Three mixed-integer programming formulations are proposed to model the problem.•A preprocessing phase and several sets of valid inequalities are...

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Bibliographic Details
Published in:European journal of operational research Vol. 303; no. 1; pp. 14 - 36
Main Authors: Baldomero-Naranjo, Marta, Kalcsics, Jörg, Marín, Alfredo, Rodríguez-Chía, Antonio M.
Format: Journal Article
Language:English
Published: Elsevier B.V 16.11.2022
Subjects:
Covering problems
Integer programming
Location
Networks
Upgrading problems
Integer programming
Covering problems
Networks
Upgrading problems
Location
ISSN:0377-2217, 1872-6860
Online Access:Get full text
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Summary:•The edge lengths are upgraded in the Maximal Covering Location Problem.•The optimal location of the facilities and the edge length reductions are decided.•Three mixed-integer programming formulations are proposed to model the problem.•A preprocessing phase and several sets of valid inequalities are developed.•The performance of the proposed formulations is tested on different datasets. We study the upgrading version of the maximal covering location problem with edge length modifications on networks. This problem aims at locating p facilities on the vertices (of the network) so as to maximise coverage, considering that the length of the edges can be reduced at a cost, subject to a given budget. Hence, we have to decide on: the optimal location of p facilities and the optimal edge length reductions. This problem is NP-hard on general graphs. To solve it, we propose three different mixed-integer formulations and a preprocessing phase for fixing variables and removing some of the constraints. Moreover, we strengthen the proposed formulations including valid inequalities. Finally, we compare the three formulations and their corresponding improvements by testing their performance over different datasets.
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2022.02.001