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dc.contributor.authorSels, Veronique
dc.contributor.authorCoelho, José
dc.contributor.authorDias, Antonio Manuel
dc.contributor.authorVanhoucke, Mario
dc.date.accessioned2017-12-02T14:52:52Z
dc.date.available2017-12-02T14:52:52Z
dc.date.issued2015
dc.identifier.doi10.1016/j.cor.2014.08.002
dc.identifier.urihttp://hdl.handle.net/20.500.12127/5021
dc.description.abstractWe consider the problem of scheduling a number of jobs on a number of unrelated parallel machines in order to minimize the makespan. We develop three heuristic approaches, i.e., a genetic algorithm, a tabu search algorithm and a hybridization of these heuristics with a truncated branch-and-bound procedure. This hybridization is made in order to accelerate the search process to near-optimal solutions. The branch-and-bound procedure will check whether the solutions obtained by the meta-heuristics can be scheduled within a tight upper bound. We compare the performances of these heuristics on a standard dataset available in the literature. Moreover, the influence of the different heuristic parameters is examined as well. The computational experiments reveal that the hybrid heuristics are able to compete with the best known results from the literature.
dc.language.isoen
dc.subjectMachine Scheduling
dc.subjectSAT
dc.titleHybrid tabu search and a truncated branch-and-bound for the unrelated parallel machine scheduling problem
dc.identifier.journalComputers and Operations Research
dc.source.volume53
dc.source.issueJan
dc.source.beginpage107
dc.source.endpage117
vlerick.knowledgedomainOperations & Supply Chain Management
vlerick.supervisor
vlerick.typearticleArticle in academic journal
vlerick.vlerickdepartmentTOM
dc.identifier.vperid140462
dc.identifier.vperid176999
dc.identifier.vperid141109
dc.identifier.vperid58614
dc.identifier.vpubid6227


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