GRASP with strategic oscillation for the α-neighbor p-center problem
This paper presents a competitive algorithm that combines the Greedy Randomized Adaptive Search Pro-cedure 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−...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universidad Pablo de Olavide (UPO) |
| Repositorio: | RIO. Repositorio Institucional Olavide |
| Idioma: | inglés |
| OAI Identifier: | oai:rio.upo.es:10433/22475 |
| Acceso en línea: | https://hdl.handle.net/10433/22475 |
| Access Level: | acceso abierto |
| Palabra clave: | Metaheuristics GRASP Tabu search Strategic oscillation p-Center Problem |
| Sumario: | This paper presents a competitive algorithm that combines the Greedy Randomized Adaptive Search Pro-cedure 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 pfacilities 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 algo- rithm 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 αvalue. |
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