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Please use this identifier to cite or link to this item: http://acervodigital.unesp.br/handle/11449/70568
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dc.contributor.authorFerreira, D.-
dc.contributor.authorFrança, P. M.-
dc.contributor.authorKimms, Alf-
dc.contributor.authorMorabito, R.-
dc.contributor.authorRangel, Socorro-
dc.contributor.authorToledo, Claudio F.M.-
dc.date.accessioned2014-05-27T11:23:39Z-
dc.date.accessioned2016-10-25T18:25:59Z-
dc.date.available2014-05-27T11:23:39Z-
dc.date.available2016-10-25T18:25:59Z-
dc.date.issued2008-09-04-
dc.identifierhttp://dx.doi.org/10.1007/978-3-540-78985-7_8-
dc.identifier.citationStudies in Computational Intelligence, v. 128, p. 169-210.-
dc.identifier.issn1860-949X-
dc.identifier.urihttp://hdl.handle.net/11449/70568-
dc.identifier.urihttp://acervodigital.unesp.br/handle/11449/70568-
dc.description.abstractThis chapter studies a two-level production planning problem where, on each level, a lot sizing and scheduling problem with parallel machines, capacity constraints and sequence-dependent setup costs and times must be solved. The problem can be found in soft drink companies where the production process involves two interdependent levels with decisions concerning raw material storage and soft drink bottling. Models and solution approaches proposed so far are surveyed and conceptually compared. Two different approaches have been selected to perform a series of computational comparisons: an evolutionary technique comprising a genetic algorithm and its memetic version, and a decomposition and relaxation approach. © 2008 Springer-Verlag Berlin Heidelberg.en
dc.format.extent169-210-
dc.language.isoeng-
dc.sourceScopus-
dc.subjectGenetic algorithm-
dc.subjectLot sizing-
dc.subjectMemetic algorithm-
dc.subjectScheduling-
dc.subjectSoft drinks industry-
dc.subjectTwo-level production planning-
dc.titleHeuristics and meta-heuristics for lot sizing and scheduling in the soft drinks industry: A comparison studyen
dc.typeoutro-
dc.contributor.institutionUniversidade Federal de São Carlos (UFSCar)-
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.contributor.institutionUniversity of Duisburg-Essen-
dc.contributor.institutionUniversidade Federal de Lavras (UFLA)-
dc.description.affiliationDepartamento de Engenharia de Produção Universidade Federal de Sao Carlos, 13565-905 Sao Carlos, SP-
dc.description.affiliationDepartamento de Matemática, Estatística e Computação Universidade Estadual Paulista, Presidente Prudente, SP 19060-400-
dc.description.affiliationDept. of Technology and Operations Management University of Duisburg-Essen, Duisburg 47048-
dc.description.affiliationDepartamento de Ciência da Computação e Estatística Universidade Estadual Paulista, Rua Cristóvão, 2265, 15054-000 S. J. do Rio Preto, SP-
dc.description.affiliationDepartamento de Ciência da Computação Universidade Federal de Lavras, 37200-000 Lavras, MG-
dc.description.affiliationUnespDepartamento de Matemática, Estatística e Computação Universidade Estadual Paulista, Presidente Prudente, SP 19060-400-
dc.description.affiliationUnespDepartamento de Ciência da Computação e Estatística Universidade Estadual Paulista, Rua Cristóvão, 2265, 15054-000 S. J. do Rio Preto, SP-
dc.identifier.doi10.1007/978-3-540-78985-7_8-
dc.rights.accessRightsAcesso restrito-
dc.relation.ispartofStudies in Computational Intelligence-
dc.identifier.scopus2-s2.0-50549102634-
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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