Cross-Entropy Method for the Maximal Covering Location Problem
[EN] The maximal covering location problem (MCLP) involves identifying optimal locations to maximize the covered demand with constraints from the number of facilities or budget limitations. This paper introduces a new MCLP formulation and a metaheuristic, the cross-entropy method, to solve the probl...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/221189 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/221189 |
| Access Level: | acceso abierto |
| Palabra clave: | Maximal covering location problem Cross-entropy method Metaheuristic Pareto sampling |
| Sumario: | [EN] The maximal covering location problem (MCLP) involves identifying optimal locations to maximize the covered demand with constraints from the number of facilities or budget limitations. This paper introduces a new MCLP formulation and a metaheuristic, the cross-entropy method, to solve the problem. The method refers to a sampling-based solution construction from statistically tractable distribution models with iterative updates via inclusion probabilities in which a Pareto order sampling and a new local search are introduced. Extensive experiments are carried out on, to our knowledge, the most complete eight benchmark data of three network types and two MCLP settings with 100-100,000 demand nodes. It demonstrates that (i) the proposed model is more compact with the number of variables and constraints, and (ii) the cross-entropy method is highly effective in finding optimal solutions and competitive with other proposals and state-ofthe-art CPLEX 20.1 considering the involved large or massive instances. |
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