Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/359243
Type: Artigo
Title: Pareto clustering search applied for 3D container ship loading plan problem
Author: Araujo, Eliseu Junio
Chaves, Antonio Augusto
Chaves, Antonio Augusto
de Salles Neto, Luiz Leduino
de Azevedo, Anibal Tavares
Abstract: The 3D Container ship Loading Plan Problem (CLPP) is an important problem that appears in seaport container terminal operations. This problem consists of determining how to organize the containers in a ship in order to minimize the number of movements necessary to load and unload the container ship and the instability of the ship in each port. The CLPP is well known to be NP-hard. In this paper, the hybrid method Pareto Clustering Search (PCS) is proposed to solve the CLPP and obtain a good approximation to the Pareto Front. The PCS aims to combine metaheuristics and local search heuristics, and the intensification is performed only in promising regions. Computational results considering instances available in the literature are presented to show that PCS provides better solutions for the CLPP than a mono-objective Simulated Annealing
Subject: Estiva
Country: Reino Unido
Editor: Elsevier
Rights: Fechado
Identifier DOI: 10.1016/j.eswa.2015.09.005
Address: https://www.sciencedirect.com/science/article/pii/S0957417415006181
Date Issue: 2016
Appears in Collections:FCA - Artigos e Outros Documentos

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