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Hybrid system for simultaneous job shop scheduling and layout optimization based on multi-agents and genetic algorithm

dc.contributor.authorAlves, Filipe
dc.contributor.authorVarela, Maria Leonilde R.
dc.contributor.authorRocha, Ana Maria A.C.
dc.contributor.authorPereira, Ana I.
dc.contributor.authorBarbosa, José
dc.contributor.authorLeitão, Paulo
dc.date.accessioned2019-05-22T10:37:55Z
dc.date.available2019-05-22T10:37:55Z
dc.date.issued2020
dc.description.abstractA challenge is emerging in the design of scheduling support systems and facility layout planning, both for manufacturing environments where dynamic adaptation and optimization become increasingly important on the efficiency and productivity. Focusing on the interactions between these two problems, this work combines two paradigms in sequential manner, optimization techniques and multi-agent systems, to better reflect practical manufacturing scenarios. This approach, in addition to significantly improve the quality of the solutions, enables fast reaction to condition changes. In such stochastic and very volatile environments, the manufacturing industries, the fast rescheduling, or planning, are crucial to maintain the system in operation. The proposed architecture was codified in MatLab and NetLogo and applied to a real-world job shop case study. The experimental results achieved optimized solutions, as well as in the responsiveness to achieve dynamic results for disruptions and simultaneously layout optimizationpt_PT
dc.description.sponsorshipThis work has been supported by FCT – Fundação para a Ciência e Tecnologia under the Project: PEst2015-2020. This work was also supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationAlves, Filipe; Varela, M. Leonilde R.; Rocha, Ana Maria A.C.; Pereira, Ana I.; Barbosa, José; Leitão, Paulo (2020). Hybrid system for simultaneous job shop scheduling and layout optimization based on multi-agents and genetic algorithm. In 18th International Conference on Hybrid Intelligent Systems. Porto. p. 387-397. ISBN 978-3-030-14347-3pt_PT
dc.identifier.doi10.1007/978-3-030-14347-3_38pt_PT
dc.identifier.issn2194-5357
dc.identifier.urihttp://hdl.handle.net/10198/19317
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectMulti-agent systemspt_PT
dc.subjectMeta-heuristicspt_PT
dc.subjectSchedulingpt_PT
dc.subjectOptimizationpt_PT
dc.titleHybrid system for simultaneous job shop scheduling and layout optimization based on multi-agents and genetic algorithmpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876/UID%2FCEC%2F00319%2F2013/PT
oaire.citation.conferencePlacePortopt_PT
oaire.citation.endPage397pt_PT
oaire.citation.startPage387pt_PT
oaire.citation.title18th International Conference on Hybrid Intelligent Systems, HIS 2018pt_PT
oaire.citation.volume157pt_PT
oaire.fundingStream5876
person.familyNameAlves
person.familyNamePereira
person.familyNameBarbosa
person.familyNameLeitão
person.givenNameFilipe
person.givenNameAna I.
person.givenNameJosé
person.givenNamePaulo
person.identifier609187
person.identifierA-8390-2011
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person.identifier.ciencia-id0716-B7C2-93E4
person.identifier.ciencia-id021B-4191-D8A5
person.identifier.ciencia-id8316-8F13-DA71
person.identifier.orcid0000-0002-8387-391X
person.identifier.orcid0000-0003-3803-2043
person.identifier.orcid0000-0003-3151-6686
person.identifier.orcid0000-0002-2151-7944
person.identifier.ridV-5791-2017
person.identifier.ridF-3168-2010
person.identifier.ridA-5468-2011
person.identifier.scopus-author-id57195267974
person.identifier.scopus-author-id15071961600
person.identifier.scopus-author-id48360905400
person.identifier.scopus-author-id35584388900
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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