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dc.rights.licenseAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.contributor.authorPeralta Abadía, José Joaquín
dc.contributor.authorCUESTA ZABALAJAUREGUI, MIKEL
dc.contributor.authorLarrinaga, Felix
dc.contributor.authorARRAZOLA, PEDRO JOSE
dc.contributor.otherTorayev, Agajan
dc.contributor.otherMartínez Arellano, Giovanna
dc.contributor.otherChaplin, Jack C.
dc.contributor.otherSanderson, David
dc.contributor.otherRatchev, Svetan
dc.date.accessioned2024-03-25T14:54:41Z
dc.date.available2024-03-25T14:54:41Z
dc.date.issued2023
dc.identifier.issn2212-8271en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=174161en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/6315
dc.description.abstractIn manufacturing, different costs must be considered when selecting the optimal manufacturing configuration. Costs include manufacturing costs, material costs, labor costs, and overhead costs. Optimal manufacturing configurations are those that minimize production criteria, such as costs, production speed, and flexibility, while still meeting the required production levels and quality standards. To find the optimal manufacturing configuration, manufacturers often use a combination of traditional techniques, e.g., mathematical modeling, simulation, and optimization, to evaluate the tradeoffs between different cost factors and identify configurations that provide the best balance between cost and performance. However, these techniques may require long development and simulation time, and/or may require expert knowledge. This paper presents a method for selecting the optimal manufacturing configuration, focusing on cost optimization, using a reinforcement learning (RL) approach for sequential decision-making. The proposed method involves developing a RL environment, requiring lower development and simulation times than traditional techniques, that captures the incurred costs, recurring costs, production rates, and setup times of manufacturing configurations. The problem is then solved using the Proximal Policy Optimization algorithm to identify the configuration that minimizes costs while still meeting the required production levels and quality standards. The effectiveness of the proposed method is validated through a machining process planning case study with multiple cost factors and production constraints. In particular, the machining process plan was developed for an industry-relevant product prototype. The results show that the proposed method can find solutions that are robust to stochastic noise, providing valuable insights for manufacturers looking to optimize manufacturing operations.en
dc.language.isoengen
dc.publisherElsevieren
dc.rights© 2023 The Authorsen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectmanufacturingen
dc.subjectoptimizationen
dc.subjectDecision-makingen
dc.subjectArtificial Intelligenceen
dc.subjectMachiningen
dc.subjectODS 9 Industria, innovación e infraestructura
dc.titleOptimal manufacturing configuration selection: sequential decision making and optimization using reinforcement learningen
dcterms.accessRightshttp://purl.org/coar/access_right/c_abf2en
dcterms.sourceProcedia CIRPen
local.contributor.groupIngeniería del software y sistemases
local.contributor.groupMecanizado de alto rendimientoes
local.description.peerreviewedtrueen
local.description.publicationfirstpage986-991en
local.description.publicationlastpage991en
local.identifier.doihttps://doi.org/10.1016/j.procir.2023.09.112en
local.contributor.otherinstitutionhttps://ror.org/01ee9ar58en
local.source.detailsVol 120. Pp. 986-991. Cape Town, South Africa. 24-26 October, 2023
oaire.format.mimetypeapplication/pdfen
oaire.file$DSPACE\assetstoreen
oaire.resourceTypehttp://purl.org/coar/resource_type/c_c94fen
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85en
oaire.funderNameComisión Europeaen
oaire.funderNameGobierno Vascoen
oaire.funderNameGobierno Vascoen
oaire.funderIdentifierhttps://ror.org/00k4n6c32 / http://data.crossref.org/fundingdata/funder/10.13039/501100000780en
oaire.funderIdentifierhttps://ror.org/00pz2fp31 / http://data.crossref.org/fundingdata/funder/10.13039/501100003086en
oaire.fundingStreamH2020en
oaire.fundingStreamIkertalde Convocatoria 2022-2023en
oaire.fundingStreamIkasiker 2022-2023en
oaire.awardNumber814078en
oaire.awardNumberIT1519-22en
oaire.awardNumberIT1443-22en
oaire.awardTitleDigital Manufacturing and Design Training Network (DimanD)en
oaire.awardTitleIngeniería de Software y Sistemasen
oaire.awardTitleMecanizado de Alto Rendimientoen
oaire.awardURISin información
oaire.awardURISin información
oaire.awardURISin información
dc.unesco.campohttp://skos.um.es/unesco6/33en
dc.unesco.disciplinahttp://skos.um.es/unesco6/3310en


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