Abstract
The Single Source Capacitated Facility Location Problem (SSCFLP) consists of determining locations for facilities to meet customer demands so that each customer must be served by a single facility. This paper proposes a matheuristic algorithm for solving large-scale SSCFLP instances that combines neighborhood-based heuristic procedures with the solution of two binary linear programming sub-problems through a general-purpose solver. The proposed algorithm starts from the optimal solution of the linear relaxation of the SSCFLP to reduce its size and identify promising potential locations for opening facilities. Computational experiments were performed on two benchmark sets of large instances. For one of them, the developed algorithm obtained optimal solutions for all instances. For the other set, it provided average relative deviations slightly lower than those of three relevant algorithms from the literature. These results allow us to conclude that the proposed algorithm generates good-quality solutions and is competitive in solving large-scale SSCFLP instances.
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Acknowledgements
The authors are grateful to the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Centro Federal de Educação Tecnológica de Minas Gerais (CEFET-MG), and Universidade Federal de Ouro Preto (UFOP) for supporting the development of the present study.
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Appendix A: Detailed results for OR-4 and TBED1 data sets
Appendix A: Detailed results for OR-4 and TBED1 data sets
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de Almeida, G.B., de Sá, E.M., de Souza, S.R. et al. A hybrid iterated local search matheuristic for large-scale single source capacitated facility location problems. J Heuristics (2023). https://doi.org/10.1007/s10732-023-09524-9
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DOI: https://doi.org/10.1007/s10732-023-09524-9