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dc.contributor.authorRivas, Danieles_ES
dc.contributor.authorQuiza, Ramónes_ES
dc.contributor.authorRivas, Marcelinoes_ES
dc.contributor.authorHaber, Rodolfo E.es_ES
dc.date.accessioned2021-02-15T13:21:07Z-
dc.date.available2021-02-15T13:21:07Z-
dc.date.issued2020-11-24-
dc.identifier.citationIEEE Acess 8: 212904-212916 (2020)es_ES
dc.identifier.other20184538-
dc.identifier.urihttp://hdl.handle.net/10261/229665-
dc.description.abstractOptimization on the basis of sustainability brings important benefits to manufacturing process as sustainable productions constitute a crucial aspect in modern manufacturing. This paper presents a new formalized framework for optimizing the sustainability of manufacturing processes. Unlike previous approaches, the proposed technique combines a methodology for selecting the sustainability indicators and a multi-objective optimization for improving the three sustainability pillars (economy, environment and society). While selecting the significant sustainability indicators in the considered manufacturing process relies on the ABC judgment method, the Saaty's method enables weighting the chosen indicators in order to combine them into suitable economic, environmental and social sustainability indexes. Other technological aspects, usually taken as objectives in previous works, are considered constraints in the proposed approach. The optimization is performed by using nature inspired heuristics, which return the set of non-dominated solutions (also known as Pareto front), from which the most convenient alternative is chosen by the decision maker, depending on the specific conditions of the process. For illustrating the usage of the proposed framework, it is applied to the optimization of a submerged arc welding process. Compared with currently used welding parameters, the computed optimal solution outperforms the economic and environmental sustainability while keeps equal the social impact. The results show not only the effectiveness of the proposed approach, but also its flexibility by giving a set of possible solutions which can be chosen depending on how are ranked the sustainability pillars.es_ES
dc.description.sponsorshipThis work was supported in part by the project Power2Power: Providing Next-Generation Silicon-Based Power Solutions in Transport and Machinery for Significant Decarbonisation in the Next Decade, funded by the Electronic Component Systems for European Leadership (ECSEL-JU) Joint Undertaking and MICINN under Grant 826417, in part by the European Commission through the Project H2020 Platform enable KITs of Artificial Intelligence for an Easy Uptake of SMEs (KITT4SME) under Grant 952119, and in part by the Cuban National Program on Basic Sciences through the Project Multi-Objectives Optimization Heuristics for Industrial Applications, under Grant P223LH001-068.es_ES
dc.language.isoenges_ES
dc.publisherInstitute of Electrical and Electronics Engineerses_ES
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/826417es_ES
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/952119es_ES
dc.relation.isversionofPublisher's versiones_ES
dc.rightsopenAccesses_ES
dc.subjectManufacturing systemses_ES
dc.subjectOptimized production technologyes_ES
dc.subjectPareto optimizationes_ES
dc.subjectSustainabilityes_ES
dc.titleTowards Sustainability of Manufacturing Processes by Multiobjective Optimization: A Case Study on a Submerged Arc Welding Processes_ES
dc.typeartículoes_ES
dc.identifier.doihttp://dx.doi.org/10.1109/ACCESS.2020.3040196-
dc.description.peerreviewedPeer reviewedes_ES
dc.relation.publisherversionhttps://doi.org/10.1109/ACCESS.2020.3040196es_ES
dc.identifier.e-issn2169-3536-
dc.rights.licensehttps://creativecommons.org/licenses/by/4.0/es_ES
dc.contributor.funderEuropean Commissiones_ES
dc.relation.csices_ES
oprm.item.hasRevisionno ko 0 false*
dc.identifier.funderhttp://dx.doi.org/10.13039/501100000780es_ES
dc.contributor.orcidQuiza, Ramón [0000-0003-1293-6044]es_ES
dc.contributor.orcidRivas, Marcelino [0000-0002-0305-515X]es_ES
dc.contributor.orcidHaber, Rodolfo E. [0000-0002-2881-0166]es_ES
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