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Main Authors: Marín, Alfredo, Martínez-Merino, Luisa I., Rodríguez-Chía, Antonio M., Saldanha-da-Gama, Francisco
Format: Preprint
Published: 2024
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Online Access:https://arxiv.org/abs/2403.07785
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author Marín, Alfredo
Martínez-Merino, Luisa I.
Rodríguez-Chía, Antonio M.
Saldanha-da-Gama, Francisco
author_facet Marín, Alfredo
Martínez-Merino, Luisa I.
Rodríguez-Chía, Antonio M.
Saldanha-da-Gama, Francisco
contents This paper introduces a very general discrete covering location model that accounts for uncertainty and time-dependent aspects. A MILP formulation is proposed for the problem. Afterwards, it is observed that most of the models existing in the literature related with covering location can be considered as particular cases of this formulation. In order to tackle large instances of this problem a Lagrangian relaxation based heuristic is developed. A computational study is addressed to check the potentials and limits of the formulation and some variants proposed for the problem, as well as to evaluate the heuristic. Finally, different measures to report the relevance of considering a multi-period stochastic setting are studied.
format Preprint
id arxiv_https___arxiv_org_abs_2403_07785
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Multi-period stochastic covering location problems: Modeling framework and solution approach
Marín, Alfredo
Martínez-Merino, Luisa I.
Rodríguez-Chía, Antonio M.
Saldanha-da-Gama, Francisco
Optimization and Control
This paper introduces a very general discrete covering location model that accounts for uncertainty and time-dependent aspects. A MILP formulation is proposed for the problem. Afterwards, it is observed that most of the models existing in the literature related with covering location can be considered as particular cases of this formulation. In order to tackle large instances of this problem a Lagrangian relaxation based heuristic is developed. A computational study is addressed to check the potentials and limits of the formulation and some variants proposed for the problem, as well as to evaluate the heuristic. Finally, different measures to report the relevance of considering a multi-period stochastic setting are studied.
title Multi-period stochastic covering location problems: Modeling framework and solution approach
topic Optimization and Control
url https://arxiv.org/abs/2403.07785