GRASP with strategic oscillation for the α-neighbor p-center problem
This paper presents a competitive algorithm that combines the Greedy Randomized Adaptive Search Procedure including a Tabu Search instead of a traditional Local Search framework, with a Strategic Oscillation post-processing, to provide high-quality solutions for the α-neighbor p-center problem (α − pCP). This problem seeks to locate p facilities to service or cover a set of n demand points with the objective of minimizing the maximum distance between each demand point and its αth nearest facility. The algorithm is compared to the best method found in the state of the art, which is an extremely efficient exact procedure for the continuous variant of the problem. An extensive comparison shows the relevance of the proposal, being able to provide competitive results independently of the α value.
This research was funded by the Spanish Ministry of “Ciencia, Innovación y Universidades” under grant ref. PGC2018-095322-B-C22, “Comunidad de Madrid” and “Fondos Estructurales” of European Union with grant refs. S2018/TCS-4566 and Y2018/EMT-5062, by the Spanish Ministry of “Economía, Industria y Competitividad through Project ECO2016-76567-C4-1-R”, and by “Junta de Andalucía”, FEDER-UPO Research and Development Call, reference number UPO-1263769.
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