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Digital twin for supply chain master planning in zero-defect manufacturing

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Digital twin for supply chain master planning in zero-defect manufacturing

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dc.contributor.author Serrano, Julio C. es_ES
dc.contributor.author Mula, Josefa es_ES
dc.contributor.author Poler, R. es_ES
dc.date.accessioned 2022-10-19T18:04:24Z
dc.date.available 2022-10-19T18:04:24Z
dc.date.issued 2021-06-30 es_ES
dc.identifier.issn 1868-4238 es_ES
dc.identifier.uri http://hdl.handle.net/10251/188312
dc.description.abstract [EN] Recently, many novel paradigms, concepts and technologies, which lay the foundation for the new revolution in manufacturing environments, have emerged and make it faster to address critical decisions today in supply chain 4.0 (SC4.0), with flexibility, resilience, sustainability and quality criteria. The current power of computational resources enables intelligent optimisation algorithms to process manufacturing data in such a way, that simulating supply chain (SC) planning performance in real time is now possible, which allows relevant information to be acquired so that SC nodes are digitally interconnected. This paper proposes a conceptual framework based on a digital twin (DT) to model, optimise and prescribe a SC¿s master production schedule (MPS) in a zero-defect environment. The proposed production technologies focus on the scientific development and resolution of new models and optimisation algorithms for the MPS problem in SC4.0. es_ES
dc.description.sponsorship The research leading to these results received funding from the EuropeanUnion H2020 Program with grant agreement No. 825631 Zero Defect Manufacturing Platform(ZDMP) and with grant agreement No. 958205 Industrial Data Services for Quality Control inSmart Manufacturing (i4Q) and from the Spanish Ministry of Science, Innovation and Universities under grant agreement RTI2018-101344-B-I00 Optimisation of zero-defects productiontechnologies enabling supply chains 4.0 (CADS4.0). es_ES
dc.language Inglés es_ES
dc.publisher Springer es_ES
dc.relation.ispartof IFIP Advances in Information and Communication Technology es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Supply chain 4.0 es_ES
dc.subject Master production schedule es_ES
dc.subject Digital twin es_ES
dc.subject Reinforcement learning es_ES
dc.subject Zero-defect manufacturing es_ES
dc.subject Conceptual framework es_ES
dc.subject.classification ORGANIZACION DE EMPRESAS es_ES
dc.title Digital twin for supply chain master planning in zero-defect manufacturing es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/978-3-030-78288-7_10 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-101344-B-I00/ES/OPTIMIZACION DE TECNOLOGIAS DE PRODUCCION CERO-DEFECTOS HABILITADORAS PARA CADENAS DE SUMINISTRO 4.0/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/825631/EU es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/958205/EU es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Organización de Empresas - Departament d'Organització d'Empreses es_ES
dc.description.bibliographicCitation Serrano, JC.; Mula, J.; Poler, R. (2021). Digital twin for supply chain master planning in zero-defect manufacturing. IFIP Advances in Information and Communication Technology. 626:102-111. https://doi.org/10.1007/978-3-030-78288-7_10 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1007/978-3-030-78288-7_10 es_ES
dc.description.upvformatpinicio 102 es_ES
dc.description.upvformatpfin 111 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 626 es_ES
dc.relation.pasarela S\446685 es_ES
dc.contributor.funder AGENCIA ESTATAL DE INVESTIGACION es_ES
dc.contributor.funder COMISION DE LAS COMUNIDADES EUROPEA es_ES
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dc.subject.ods 09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación es_ES


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