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A hybrid metaheuristic for the Bus Driver Rostering Problem

dc.contributor.authorBarbosa, Vítor
dc.contributor.authorRespício, A.
dc.contributor.authorAlvelos, F.
dc.date.accessioned2014-09-26T11:53:50Z
dc.date.available2014-09-26T11:53:50Z
dc.date.issued2013-02
dc.descriptionComunicação apresentada 2nd International Conference on Operations Research and Enterprise Systems, 16-18 fevereiro 2013, Barcelona, Espanhapor
dc.description.abstractThis paper presents a new decomposition model for the Bus Driver Rostering Problem and proposes the hybridization of column generation and genetic algorithms to achieve good quality rosters in short time. The decomposition model is based on the definition of a subproblem for each driver, which is responsible for the creation of valid work-schedules for the rostering period. Column generation is used to obtain an optimal linear solution. This solution and the subproblems' solutions obtained during the column generation are then used by the genetic algorithm to find good quality combinations of drivers' schedules, i.e. good quality rosters. Computational tests show the efficiency and effectiveness of the proposed approach.por
dc.identifier.urihttp://hdl.handle.net/10400.26/6738
dc.language.isoengpor
dc.peerreviewedyespor
dc.subjectColumn generationpor
dc.subjectGenetic algorithmspor
dc.subjectHybrid metaheuristicpor
dc.subjectRosteringpor
dc.titleA hybrid metaheuristic for the Bus Driver Rostering Problempor
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceBarcelona, Espanhapor
oaire.citation.titleInternational Conference on Operations Research and Entrepise Systems (ICORES)por
person.familyNameBarbosa
person.givenNameVítor
person.identifier.ciencia-idD715-8855-7F5F
person.identifier.orcid0000-0002-1641-7834
person.identifier.ridO-2318-2016
person.identifier.scopus-author-id55735355600
rcaap.rightsopenAccesspor
rcaap.typeconferenceObjectpor
relation.isAuthorOfPublicationeb482ee1-a9d7-4658-aa79-5be790aa90b4
relation.isAuthorOfPublication.latestForDiscoveryeb482ee1-a9d7-4658-aa79-5be790aa90b4

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