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dc.creatorYepes-Borrero, Juan C.es
dc.creatorPerea Rojas-Marcos, Federicoes
dc.creatorVilla Julià, Fulgenciaes
dc.creatorVallada Regalada, Evaes
dc.date.accessioned2023-05-26T10:06:25Z
dc.date.available2023-05-26T10:06:25Z
dc.date.issued2023-06
dc.identifier.issn0305-0548es
dc.identifier.issn1873-765Xes
dc.identifier.urihttps://hdl.handle.net/11441/146680
dc.description.abstractMachine scheduling problems arise in many production processes, and are something that needs to be consider when optimizing the supply chain. Among them, flowshop scheduling problems happen when a number of jobs have to be sequentially processed by a number of machines. This paper addressees, for the first time, the Permutation Flowshop Scheduling problem with additional Resources during Setups (PFSR-S). In this problem, in addition to the standard permutation flowshop constraints, each machine requires a setup between the processing of two consecutive jobs. A number of additional and scarce resources, e.g. operators, are needed to carry out each setup. Two Mixed Integer Linear Programming formulations and an exact algorithm are proposed to solve the PFSR-S. Due to its complexity, these approaches can only solve instances of small size to optimality. Therefore, a GRASP metaheuristic is also proposed which provides solutions for much larger instances. All the methods designed for the PFSR-S in this paper are computationally tested over a benchmark of instances adapted from the literature. The results obtained show that the GRASP metaheuristic finds good quality solutions in short computational times.es
dc.formatapplication/pdfes
dc.format.extent10 p.es
dc.language.isoenges
dc.publisherElsevieres
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectSchedulinges
dc.subjectFlowshopes
dc.subjectMathematical programminges
dc.subjectGRASPes
dc.titleFlowshop with additional resources during setups: Mathematical models and a GRASP algorithmes
dc.typeinfo:eu-repo/semantics/articlees
dcterms.identifierhttps://ror.org/03yxnpp24
dc.type.versioninfo:eu-repo/semantics/publishedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Matemática Aplicada II (ETSI)es
dc.relation.projectIDPID2020-114594GB-C21es
dc.relation.projectIDFEDER-US-1256951es
dc.relation.projectIDAT 21_00032es
dc.relation.projectIDP18-FR-1422es
dc.relation.projectIDPID2021-124975OB-I00es
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0305054823000564es
dc.identifier.doi10.1016/j.cor.2023.106192es
dc.contributor.groupUniversidad de Sevilla. FQM241: Grupo de Investigación en Localizaciónes
dc.journaltitleComputers and Operations Researches
dc.publication.volumen154es
dc.publication.issue106192es
dc.contributor.funderAgencia Estatal de Investigación (AEI) and the European Regional Development’s fund (ERDF): PID2020-114594GB-C21es
dc.contributor.funderRegional Government of Andalusia: project FEDER-US-1256951es
dc.contributor.funderRegional Government of Andalusia: project AT 21_00032es
dc.contributor.funderRegional Government of Andalusia: project P18-FR-1422es
dc.contributor.funderSpanish Ministry of Science and Innovation under the project “OPRES-Realistic Optimization in Problems in Public Health” and FEDER No. PID2021-124975OB-I00es

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