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

Detalhes bibliográficos
Autor(a) principal: De Azevedo, Anibal Tavares
Data de Publicação: 2014
Outros Autores: Ribeiro, Cassilda Maria, De Sena, Galeno José, Chaves, Antônio Augusto, Neto, Luis Leduíno Salles, Moretti, Antônio Carlos
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1504/IJDATS.2014.063060
http://hdl.handle.net/11449/231337
Resumo: 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.
id UNSP_191b71d56bc83b81d48e9d2cd6922feb
oai_identifier_str oai:repositorio.unesp.br:11449/231337
network_acronym_str UNSP
network_name_str Repositório Institucional da UNESP
repository_id_str 2946
spelling Solving the 3D container ship loading planning problem by representation by rules and meta-heuristics3D Container ship stowageCombinatorial optimisationMeta-heuristicThis 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.Applied Science Faculty State University of Campinas, Rua Pedro Zaccaria, 1300, LimeiraMathematics Department State of São Paulo University, Av. Dr. Ariberto Pereira da Cunha, 333, GuaratinguetáDepartment of Science and Technology Federal University of São Paulo, Rua Talim, 330, São PauloUniversidade Estadual de Campinas (UNICAMP)Universidade de São Paulo (USP)De Azevedo, Anibal TavaresRibeiro, Cassilda MariaDe Sena, Galeno JoséChaves, Antônio AugustoNeto, Luis Leduíno SallesMoretti, Antônio Carlos2022-04-29T08:44:51Z2022-04-29T08:44:51Z2014-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article228-260http://dx.doi.org/10.1504/IJDATS.2014.063060International Journal of Data Analysis Techniques and Strategies, v. 6, n. 3, p. 228-260, 2014.1755-80691755-8050http://hdl.handle.net/11449/23133710.1504/IJDATS.2014.0630602-s2.0-84904756065Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengInternational Journal of Data Analysis Techniques and Strategiesinfo:eu-repo/semantics/openAccess2022-04-29T08:44:51Zoai:repositorio.unesp.br:11449/231337Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462022-04-29T08:44:51Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Solving the 3D container ship loading planning problem by representation by rules and meta-heuristics
title Solving the 3D container ship loading planning problem by representation by rules and meta-heuristics
spellingShingle Solving the 3D container ship loading planning problem by representation by rules and meta-heuristics
De Azevedo, Anibal Tavares
3D Container ship stowage
Combinatorial optimisation
Meta-heuristic
title_short Solving the 3D container ship loading planning problem by representation by rules and meta-heuristics
title_full Solving the 3D container ship loading planning problem by representation by rules and meta-heuristics
title_fullStr Solving the 3D container ship loading planning problem by representation by rules and meta-heuristics
title_full_unstemmed Solving the 3D container ship loading planning problem by representation by rules and meta-heuristics
title_sort Solving the 3D container ship loading planning problem by representation by rules and meta-heuristics
author De Azevedo, Anibal Tavares
author_facet De Azevedo, Anibal Tavares
Ribeiro, Cassilda Maria
De Sena, Galeno José
Chaves, Antônio Augusto
Neto, Luis Leduíno Salles
Moretti, Antônio Carlos
author_role author
author2 Ribeiro, Cassilda Maria
De Sena, Galeno José
Chaves, Antônio Augusto
Neto, Luis Leduíno Salles
Moretti, Antônio Carlos
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual de Campinas (UNICAMP)
Universidade de São Paulo (USP)
dc.contributor.author.fl_str_mv De Azevedo, Anibal Tavares
Ribeiro, Cassilda Maria
De Sena, Galeno José
Chaves, Antônio Augusto
Neto, Luis Leduíno Salles
Moretti, Antônio Carlos
dc.subject.por.fl_str_mv 3D Container ship stowage
Combinatorial optimisation
Meta-heuristic
topic 3D Container ship stowage
Combinatorial optimisation
Meta-heuristic
description 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.
publishDate 2014
dc.date.none.fl_str_mv 2014-01-01
2022-04-29T08:44:51Z
2022-04-29T08:44:51Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1504/IJDATS.2014.063060
International Journal of Data Analysis Techniques and Strategies, v. 6, n. 3, p. 228-260, 2014.
1755-8069
1755-8050
http://hdl.handle.net/11449/231337
10.1504/IJDATS.2014.063060
2-s2.0-84904756065
url http://dx.doi.org/10.1504/IJDATS.2014.063060
http://hdl.handle.net/11449/231337
identifier_str_mv International Journal of Data Analysis Techniques and Strategies, v. 6, n. 3, p. 228-260, 2014.
1755-8069
1755-8050
10.1504/IJDATS.2014.063060
2-s2.0-84904756065
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv International Journal of Data Analysis Techniques and Strategies
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 228-260
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
_version_ 1799965194257956864

Registros relacionados