Solving the 3D container ship loading planning problem by representation by rules and meta-heuristics
This 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 formulatio...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2014 |
| País: | Brasil |
| Institución: | Universidade Estadual Paulista (UNESP) |
| Repositorio: | Repositório Institucional da UNESP |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.unesp.br:11449/231337 |
| Acceso en línea: | http://dx.doi.org/10.1504/IJDATS.2014.063060 http://hdl.handle.net/11449/231337 |
| Access Level: | acceso abierto |
| Palabra clave: | 3D Container ship stowage Combinatorial optimisation Meta-heuristic |
| Sumario: | This 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. |
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