Navegando por Palavras-chave "Clustering Search"

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    Clustering search
    (Sociedade Brasileira de Pesquisa Operacional, 2013-04-01) Oliveira, Alexandre César Muniz De; Chaves, Antonio Augusto [UNIFESP]; Lorena, Luiz Antonio Nogueira; Universidade Federal do Maranhão; Universidade Federal de São Paulo (UNIFESP); Instituto Nacional de Pesquisas Espaciais
    This paper presents the Clustering Search (CS) as a new hybrid metaheuristic, which works in conjunction with other metaheuristics, managing the implementation of local search algorithms for optimization problems. Usually the local search is costly and should be used only in promising regions of the search space. The CS assists in the discovery of these regions by dividing the search space into clusters. The CS and its applications are reviewed and a case study for a problem of capacitated clustering is presented.
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    Hybrid metaheuristics to solve a multi-product two-stage capacitated facility location problem
    (Wiley, 2021) Chaves, Antonio Augusto [UNIFESP]; Mauri, Geraldo Regis; Biajoli, Fabricio Lacerda [UNIFESP]; Rabello, Rômulo Louzada; Ribeiro, Glaydston Mattos; Lorena, Luiz Antônio Nogueira [UNIFESP]; http://lattes.cnpq.br/4973949421738244
    This paper presents two hybrid metaheuristics to solve a multi-product two-stage capacitated facility location prob- lem (MP-TSCFLP). In this problem, a set of different products must be transported from a set of plants to a set of intermediate depots (first stage) and from these depots to a set of customers (second stage). The objective is to minimize the cost related to open plants and depots plus the cost for transporting the products from the plants until the customers satisfying demand and capacity constraints. Recently, the methods Clustering Search (CS) and Biased Random-Key Genetic Algorithm (BRKGA) were successfully applied to solve a single-product problem (SP-TSCFLP). Therefore, in this paper we propose adaptations and implementations of these methods for handling with a multi-product approach. To the best of our knowledge, CS and BRKGA presented the best results for the SP-TSCFLP and both have not yet been applied to solve the problem with multiple products. Four sets of large- sized instances with different characteristics are proposed and computational experiments compare the obtained results to those from a commercial solver.