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

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

Author Araujo, Eliseu Junio Autor UNIFESP Google Scholar
Chaves, Antonio Augusto Autor UNIFESP Google Scholar
Salles Neto, Luiz Leduino de Autor UNIFESP Google Scholar
de Azevedo, Anibal Tavares Google Scholar
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. (C) 2015 Elsevier Ltd. All rights reserved.
Keywords Stowage planning
Hybrid heuristics
Clustering search
Pareto front
xmlui.dri2xhtml.METS-1.0.item-coverage Oxford
Language English
Sponsor FAPESP
Grant number FAPESP: 2012/17523-3
Date 2016
Published in Expert Systems With Applications. Oxford, v. 44, p. 50-57, 2016.
ISSN 0957-4174 (Sherpa/Romeo, impact factor)
Publisher Pergamon-Elsevier Science Ltd
Extent 50-57
Access rights Closed access
Type Article
Web of Science ID WOS:000365051500005

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