Solving the 3D container ship loading planning problem by representation by rules and meta-heuristics

dc.contributorUniversidade Estadual de Campinas (UNICAMP)
dc.contributorUniversidade de São Paulo (USP)
dc.date.accessioned2022-04-29T08:44:51Z
dc.date.accessioned2022-12-20T03:14:44Z
dc.date.available2022-04-29T08:44:51Z
dc.date.available2022-12-20T03:14:44Z
dc.date.created2022-04-29T08:44:51Z
dc.date.issued2014-01-01
dc.identifierInternational Journal of Data Analysis Techniques and Strategies, v. 6, n. 3, p. 228-260, 2014.
dc.identifier1755-8069
dc.identifier1755-8050
dc.identifierhttp://hdl.handle.net/11449/231337
dc.identifier10.1504/IJDATS.2014.063060
dc.identifier2-s2.0-84904756065
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5411471
dc.description.abstractThis paper formulates the 3D containership loading planning problem (3D CLPP) and also proposes a new and compact representation to efficiently solve it. The key objective of stowage planning is to minimise the number of container movements and also the ship's instability. The binary formulation of this problem is properly described and an alternative formulation called Representation by Rules is proposed. This new representation is combined with three metaheuristics-genetic algorithm, simulated annealing, and beam search-to solve the 3D CLPP in a manner that ensures that every solution analysed in the optimisation process is compact and feasible. © 2014 Inderscience Enterprises Ltd.
dc.languageeng
dc.relationInternational Journal of Data Analysis Techniques and Strategies
dc.sourceScopus
dc.subject3D Container ship stowage
dc.subjectCombinatorial optimisation
dc.subjectMeta-heuristic
dc.titleSolving the 3D container ship loading planning problem by representation by rules and meta-heuristics
dc.typeArtículos de revistas


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