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
- Institución
- Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
- OAI Identificador
- oai:digital.cic.gba.gob.ar:11746/11589
Ver los metadatos del registro completo
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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 |
language |
spa |
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/ |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:CIC Digital (CICBA) instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Aires instacron:CICBA |
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CIC Digital (CICBA) |
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CIC Digital (CICBA) |
instname_str |
Comisión de Investigaciones Científicas de la Provincia de Buenos Aires |
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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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