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dc.contributor.authorBeojone, Caio Vitor [UNESP]
dc.contributor.authorMáximo De Souza, Regiane [UNESP]
dc.date.accessioned2020-12-12T01:26:37Z
dc.date.available2020-12-12T01:26:37Z
dc.date.issued2020-01-01
dc.identifierhttp://dx.doi.org/10.1590/0101-7438.2020.040.00220764
dc.identifier.citationPesquisa Operacional, v. 40.
dc.identifier.issn1678-5142
dc.identifier.issn0101-7438
dc.identifier.urihttp://hdl.handle.net/11449/198954
dc.description.abstractWe improve the shift-scheduling process by using nonstationary queueing models to evaluate schedules and two heuristics to generate schedules. Firstly, we improved the fitness function and the initial population generation method for a benchmark genetic algorithm in the literature. We also proposed a simple local search heuristic. The improved genetic algorithm found solutions that obey the delay probability constraint more often. The proposed local search heuristic also finds feasible solutions with a much lower computational expense, especially under low arrival rates. Differently from a genetic algorithm, the local search heuristic does not rely on random choices. Furthermore, it finds one final solution from one initial solution, rather than from a population of solutions. The developed local search heuristic works with only one well-defined goal, making it simple and straightforward to implement. Nevertheless, the code for the heuristic is simple enough to accept changes and cope with multiple objectives.en
dc.language.isoeng
dc.relation.ispartofPesquisa Operacional
dc.sourceScopus
dc.subjectGenetic algorithm
dc.subjectLocal search heuristic
dc.subjectNonstationary queues
dc.titleImproving the shift-scheduling problem using non-stationary queueing models with local heuristic and genetic algorithmen
dc.typeArtigo
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.description.affiliationDepartment of Production Engineering São Paulo State University – UNESP
dc.description.affiliationUnespDepartment of Production Engineering São Paulo State University – UNESP
dc.identifier.doi10.1590/0101-7438.2020.040.00220764
dc.identifier.scieloS0101-74382020000100201
dc.rights.accessRightsAcesso aberto
dc.identifier.scopus2-s2.0-85086048999
dc.identifier.fileS0101-74382020000100201.pdf
unesp.author.orcid0000-0002-6491-7104[1]
unesp.author.orcid0000-0002-4695-2678[2]
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