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Production scheduling using multi-objective optimization and cluster approaches

dc.contributor.authorAzevedo, Beatriz Flamia
dc.contributor.authorVarela, Maria Leonilde R.
dc.contributor.authorPereira, Ana I.
dc.date.accessioned2023-02-28T15:38:56Z
dc.date.available2023-02-28T15:38:56Z
dc.date.issued2022
dc.description.abstractProduction scheduling is a crucial task in the manufacturing process. In this way, the managers need to make decisions about the jobs production schedule. However, this task is not simple to perform, often requiring complex software tools and specialized algorithms to find the optimal solution. This work considers a multi-objective optimization algorithm to explore the production scheduling performance measure in order to help managers in decision making related to jobs attribution in a set of parallel machines. For this, five important production scheduling performance measures (makespan, tardiness and earliness time, number of tardy and early jobs) were combined into three objective functions and the Pareto front generated was analyzed by cluster techniques. The results presented different combinations to optimize the production process, providing to the manager different possibilities to prioritize the objectives considered.pt_PT
dc.description.sponsorshipThis work has been supported by FCT – Funda¸c˜ao para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020 and EXPL/EMESIS/1224/2021. Beatriz Flamia Azevedo is supported by FCT Grant Reference SFRH/BD/07427/2021.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationAzevedo, Beatriz Flamia; Varela, Maria Leonilde R.; Pereira, Ana I. (2022). Production scheduling using multi-objective optimization and cluster approaches. In 12th International Conference on Innovations in Bio-Inspired Computing and Applications, IBICA 2021 and 11th World Congress on Information and Communication Technologies, WICT 2021.pt_PT
dc.identifier.doi10.1007/978-3-030-96299-9_12pt_PT
dc.identifier.issn23673370
dc.identifier.urihttp://hdl.handle.net/10198/27319
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relationALGORITMI Research Center
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectK-meanspt_PT
dc.subjectNSGApt_PT
dc.subjectParallel machinespt_PT
dc.subjectSimulationpt_PT
dc.titleProduction scheduling using multi-objective optimization and cluster approachespt_PT
dc.typeconference paper
dspace.entity.typePublication
oaire.awardTitleALGORITMI Research Center
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00319%2F2020/PT
oaire.citation.endPage129pt_PT
oaire.citation.startPage120pt_PT
oaire.citation.title12th International Conference on Innovations in Bio-Inspired Computing and Applications, IBICA 2021 and 11th World Congress on Information and Communication Technologies, WICT 2021pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameAzevedo
person.familyNamePereira
person.givenNameBeatriz Flamia
person.givenNameAna I.
person.identifier.ciencia-id181E-855C-E62C
person.identifier.ciencia-id0716-B7C2-93E4
person.identifier.orcid0000-0002-8527-7409
person.identifier.orcid0000-0003-3803-2043
person.identifier.ridF-3168-2010
person.identifier.scopus-author-id15071961600
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsrestrictedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublication04fa4023-3726-4dd5-8d97-f6b162ceb820
relation.isAuthorOfPublicatione9981d62-2a2b-4fef-b75e-c2a14b0e7846
relation.isAuthorOfPublication.latestForDiscovery04fa4023-3726-4dd5-8d97-f6b162ceb820
relation.isProjectOfPublication0d98f999-8fd3-46a8-8a71-a7ff478a1207
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