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Bio-inspired multi-objective algorithms applied on production scheduling problems

2023Journal article Restricted access
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dc.contributor.authorAzevedo, Beatriz Flamia
dc.contributor.authorVega, Rubén
dc.contributor.authorVarela, Maria Leonilde Rocha
dc.contributor.authorPereira, Ana I.
dc.date.accessioned2023-02-28T15:35:33Z
dc.date.available2023-02-28T15:35:33Z
dc.date.issued2023
dc.description.abstractProduction scheduling is a crucial task in the manufacturing process. In this way, the managers must decide the job's production schedule. However, this task is not simple, often requiring complex software tools and specialized algorithms to find the optimal solution. In this work, a multi-objective optimization model was developed to explore production scheduling performance measures to help managers in decision-making related to job attribution under three simulations of parallel machine scenarios. Five important production scheduling performance measures were considered (makespan, tardiness and earliness times, number of tardy and early jobs), and combined into three objective functions. To solve the scheduling problem, three multi-objective evolutionary algorithms are considered (Multi-objective Particle Swarm Optimization, Multi-objective Grey Wolf Algorithm, and, Non-dominated Sorting Genetic Algorithm II), and the set of optimum solutions named Pareto Front, provided by each one is compared in terms of dominance, generating a new Pareto Front, denoted as Final Pareto Front. Furthermore, this Final Pareto Front is analysed through an automatic bio-inspired clustering algorithm based on the Genetic Algorithm. The results demonstrated that the proposed approach efficiently solves the scheduling problem considered. In addition, the proposed methodology provided more robust solutions by combining different bio-inspired multi-objective techniques. Furthermore, the cluster analysis proved fundamental for a better understanding of the results and support for choosing the final optimum solution.pt_PT
dc.description.sponsorshipThis work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020 and EXPL/EME-SIS/1224/2021. Beatriz Flamia Azevedo is supported by FCT Grant Reference SFRH/BD/07427/2021 The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationAzevedo, Beatriz Flamia; Vega, Rubén; Varela, Maria Leonilde Rocha; Pereira, Ana I. (2023). Bio-inspired multi-objective algorithms applied on production scheduling problems. International Journal of Industrial Engineering Computations. ISSN 1923-2926. 14:2, p. 415-436pt_PT
dc.identifier.doi10.5267/j.ijiec.2022.12.001
dc.identifier.urihttp://hdl.handle.net/10198/27318
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationALGORITMI Research Center
dc.relationCollaborative Platform based on Data Normalization Techniques and Dynamic Fuzzy Multi-criteria approaches for Integrated Manufacturing and Maintenance Information Processing
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectBio-inspired algorithmspt_PT
dc.subjectMetaheuristicpt_PT
dc.subjectProduction schedulingpt_PT
dc.subjectDecision supportpt_PT
dc.subjectMulti-objectivept_PT
dc.subjectClustering algorithmpt_PT
dc.titleBio-inspired multi-objective algorithms applied on production scheduling problemspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleALGORITMI Research Center
oaire.awardTitleCollaborative Platform based on Data Normalization Techniques and Dynamic Fuzzy Multi-criteria approaches for Integrated Manufacturing and Maintenance Information Processing
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00319%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/EXPL%2FEME-SIS%2F1224%2F2021/PT
oaire.citation.titleInternational Journal of Industrial Engineering Computationspt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream3599-PPCDT
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.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsrestrictedAccesspt_PT
rcaap.typearticlept_PT
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