Abstract
Facility Location Problems (FLPs) have been widely studied in the fields of Operations Research and Computer Science. This is due to the fact that FLPs have numerous practical applications in different areas, from logistics (e.g., placement of distribution or retailing centers) to Internet computing (e.g., placement of cloud-service servers on a distributed network). In this paper we propose a biased iterated local search algorithm for solving the uncapacitated version of the FLP. Biased randomization of heuristics has been successfully applied in the past to solve other combinatorial optimization problems in logistics, transportation, and production -e.g., different vehicle and arc routing problems as well as scheduling problems. Our approach integrates a biased randomization within an Iterated Local Search framework. Several standard benchmarks from the literature have been used to prove the quality and efficiency of the proposed algorithm.
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Acknowledgments
This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (TRA2013-48180-C3-P) and FEDER. Likewise we want to acknowledge the support received by the Department of Universities, Research & Information Society of the Catalan Government (2014-CTP-00001).
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de Armas, J., Juan, A.A., Marquès, J.M. (2018). A Biased-Randomized Algorithm for the Uncapacitated Facility Location Problem. In: Gil-Lafuente, A., Merigó, J., Dass, B., Verma, R. (eds) Applied Mathematics and Computational Intelligence. FIM 2015. Advances in Intelligent Systems and Computing, vol 730. Springer, Cham. https://doi.org/10.1007/978-3-319-75792-6_22
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DOI: https://doi.org/10.1007/978-3-319-75792-6_22
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