Pareto clustering search applied for 3D container ship loading plan problem

Autor(es)

Araujo, Eliseu Junio ^[1] ; Chaves, Antonio Augusto ^[1] ; de Salles Neto, Luiz Leduino ^[1] ; de Azevedo, Anibal Tavares ^[2]

Afiliação do(s) autor(es)

^[1] Univ Fed Sao Paulo, Sao Jose Dos Campos - Brazil

^[2] Univ Estadual Campinas, Limeira - Brazil

• Tipo de documento: Artigo Científico
• Fonte: EXPERT SYSTEMS WITH APPLICATIONS; v. 44, p. 50-57, FEB 2016.
• Citações Web of Science: 5

Resumo

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. (C) 2015 Elsevier Ltd. All rights reserved. (AU)

• Processo FAPESP: 12/17523-3 - Novos métodos híbridos para resolução de problemas de otimização combinatória
• Beneficiário: Antônio Augusto Chaves
• Modalidade de apoio: Auxílio à Pesquisa - Jovens Pesquisadores