Optimización y control del flujo de materiales en procesos de producción flexibles utilizando aprendizaje profundo

Autores
Saavedra Sueldo, Carolina; Perez Colo, Ivo; De Paula, Mariano; Villar, Sebastián; Acosta, Gerardo G.
Año de publicación
2021
Idioma
español castellano
Tipo de recurso
documento de conferencia
Estado
versión aceptada
Descripción
Industry 4.0, currently on the rise, demandsincreasing flexibility and adaptation of production systems tochanging products demands and external factors. The adaptationof the production systems implies frequent and often abruptchanges in the configurations of the shop floors and consequentlythe movement of materials must be re-planned. Materialhandlingis significant in terms of operative costs and times and it doesnot add value to the end products. It is desired to optimize theperformance of the system based onthe degree of movements,buffer usage and waiting times, such that the combinationof these minimizes the impact on the process costs. Machinelearning algorithms incombination with powerful computationalsimulators can be mutually leveraged to give rise to solvethese kinds of real-world problems, typical of smart factories.In this work, for the optimization approach, we develop aclosed-loop decision-making system with a deep reinforcementlearning algorithm based on a discrete-event simulation modelfor material handling. In addition, our proposed approach usesthe communication architecture Simulai, which allows interfacinga computational discrete-event simulator and the proposed deep learning-based decision-making algorithm. The functionality ofour proposal is evidenced through the obtained results and anoptimal solution for the problem stated is reached, proving thatan intelligent agentcan collaborate in making multiple decisionsfor smart factories.
Materia
Ingenierías y Tecnologías
Industry 4.0
Autonomous Decision System
Deep Reinforcement Learning
Optimization
Material Handling
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
CIC Digital (CICBA)
Institución
Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
OAI Identificador
oai:digital.cic.gba.gob.ar:11746/11589

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network_name_str CIC Digital (CICBA)
spelling Optimización y control del flujo de materiales en procesos de producción flexibles utilizando aprendizaje profundoSaavedra Sueldo, CarolinaPerez Colo, IvoDe Paula, MarianoVillar, SebastiánAcosta, Gerardo G.Ingenierías y TecnologíasIndustry 4.0Autonomous Decision SystemDeep Reinforcement LearningOptimizationMaterial HandlingIndustry 4.0, currently on the rise, demandsincreasing flexibility and adaptation of production systems tochanging products demands and external factors. The adaptationof the production systems implies frequent and often abruptchanges in the configurations of the shop floors and consequentlythe movement of materials must be re-planned. Materialhandlingis significant in terms of operative costs and times and it doesnot add value to the end products. It is desired to optimize theperformance of the system based onthe degree of movements,buffer usage and waiting times, such that the combinationof these minimizes the impact on the process costs. Machinelearning algorithms incombination with powerful computationalsimulators can be mutually leveraged to give rise to solvethese kinds of real-world problems, typical of smart factories.In this work, for the optimization approach, we develop aclosed-loop decision-making system with a deep reinforcementlearning algorithm based on a discrete-event simulation modelfor material handling. In addition, our proposed approach usesthe communication architecture Simulai, which allows interfacinga computational discrete-event simulator and the proposed deep learning-based decision-making algorithm. The functionality ofour proposal is evidenced through the obtained results and anoptimal solution for the problem stated is reached, proving thatan intelligent agentcan collaborate in making multiple decisionsfor smart factories.2021-11info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttps://digital.cic.gba.gob.ar/handle/11746/11589spainfo:eu-repo/semantics/altIdentifier/isbn/978-987-88-2891-6info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/reponame:CIC Digital (CICBA)instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Airesinstacron:CICBA2025-09-29T13:40:01Zoai:digital.cic.gba.gob.ar:11746/11589Institucionalhttp://digital.cic.gba.gob.arOrganismo científico-tecnológicoNo correspondehttp://digital.cic.gba.gob.ar/oai/snrdmarisa.degiusti@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:94412025-09-29 13:40:02.125CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Airesfalse
dc.title.none.fl_str_mv Optimización y control del flujo de materiales en procesos de producción flexibles utilizando aprendizaje profundo
title Optimización y control del flujo de materiales en procesos de producción flexibles utilizando aprendizaje profundo
spellingShingle Optimización y control del flujo de materiales en procesos de producción flexibles utilizando aprendizaje profundo
Saavedra Sueldo, Carolina
Ingenierías y Tecnologías
Industry 4.0
Autonomous Decision System
Deep Reinforcement Learning
Optimization
Material Handling
title_short Optimización y control del flujo de materiales en procesos de producción flexibles utilizando aprendizaje profundo
title_full Optimización y control del flujo de materiales en procesos de producción flexibles utilizando aprendizaje profundo
title_fullStr Optimización y control del flujo de materiales en procesos de producción flexibles utilizando aprendizaje profundo
title_full_unstemmed Optimización y control del flujo de materiales en procesos de producción flexibles utilizando aprendizaje profundo
title_sort Optimización y control del flujo de materiales en procesos de producción flexibles utilizando aprendizaje profundo
dc.creator.none.fl_str_mv Saavedra Sueldo, Carolina
Perez Colo, Ivo
De Paula, Mariano
Villar, Sebastián
Acosta, Gerardo G.
author Saavedra Sueldo, Carolina
author_facet Saavedra Sueldo, Carolina
Perez Colo, Ivo
De Paula, Mariano
Villar, Sebastián
Acosta, Gerardo G.
author_role author
author2 Perez Colo, Ivo
De Paula, Mariano
Villar, Sebastián
Acosta, Gerardo G.
author2_role author
author
author
author
dc.subject.none.fl_str_mv Ingenierías y Tecnologías
Industry 4.0
Autonomous Decision System
Deep Reinforcement Learning
Optimization
Material Handling
topic Ingenierías y Tecnologías
Industry 4.0
Autonomous Decision System
Deep Reinforcement Learning
Optimization
Material Handling
dc.description.none.fl_txt_mv Industry 4.0, currently on the rise, demandsincreasing flexibility and adaptation of production systems tochanging products demands and external factors. The adaptationof the production systems implies frequent and often abruptchanges in the configurations of the shop floors and consequentlythe movement of materials must be re-planned. Materialhandlingis significant in terms of operative costs and times and it doesnot add value to the end products. It is desired to optimize theperformance of the system based onthe degree of movements,buffer usage and waiting times, such that the combinationof these minimizes the impact on the process costs. Machinelearning algorithms incombination with powerful computationalsimulators can be mutually leveraged to give rise to solvethese kinds of real-world problems, typical of smart factories.In this work, for the optimization approach, we develop aclosed-loop decision-making system with a deep reinforcementlearning algorithm based on a discrete-event simulation modelfor material handling. In addition, our proposed approach usesthe communication architecture Simulai, which allows interfacinga computational discrete-event simulator and the proposed deep learning-based decision-making algorithm. The functionality ofour proposal is evidenced through the obtained results and anoptimal solution for the problem stated is reached, proving thatan intelligent agentcan collaborate in making multiple decisionsfor smart factories.
description Industry 4.0, currently on the rise, demandsincreasing flexibility and adaptation of production systems tochanging products demands and external factors. The adaptationof the production systems implies frequent and often abruptchanges in the configurations of the shop floors and consequentlythe movement of materials must be re-planned. Materialhandlingis significant in terms of operative costs and times and it doesnot add value to the end products. It is desired to optimize theperformance of the system based onthe degree of movements,buffer usage and waiting times, such that the combinationof these minimizes the impact on the process costs. Machinelearning algorithms incombination with powerful computationalsimulators can be mutually leveraged to give rise to solvethese kinds of real-world problems, typical of smart factories.In this work, for the optimization approach, we develop aclosed-loop decision-making system with a deep reinforcementlearning algorithm based on a discrete-event simulation modelfor material handling. In addition, our proposed approach usesthe communication architecture Simulai, which allows interfacinga computational discrete-event simulator and the proposed deep learning-based decision-making algorithm. The functionality ofour proposal is evidenced through the obtained results and anoptimal solution for the problem stated is reached, proving thatan intelligent agentcan collaborate in making multiple decisionsfor smart factories.
publishDate 2021
dc.date.none.fl_str_mv 2021-11
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/acceptedVersion
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://digital.cic.gba.gob.ar/handle/11746/11589
url https://digital.cic.gba.gob.ar/handle/11746/11589
dc.language.none.fl_str_mv spa
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dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/978-987-88-2891-6
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
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dc.source.none.fl_str_mv reponame:CIC Digital (CICBA)
instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
instacron:CICBA
reponame_str CIC Digital (CICBA)
collection CIC Digital (CICBA)
instname_str Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
instacron_str CICBA
institution CICBA
repository.name.fl_str_mv CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
repository.mail.fl_str_mv marisa.degiusti@sedici.unlp.edu.ar
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