SmartGantt - An intelligent system for real time rescheduling based on relational reinforcement learning

Autores
Palombarini, Jorge Andrés; Martínez, Ernesto Carlos
Año de publicación
2012
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
With the current trend towards cognitive manufacturing systems to deal with unforeseen events and disturbances that constantly demand real-time repair decisions, learning/reasoning skills and interactive capabilities are important functionalities for rescheduling a shop-floor on the fly taking into account several objectives and goal states. In this work, the automatic generation and update through learning of rescheduling knowledge using simulated transitions of abstract schedule states is proposed. Deictic representations of schedules based on focal points are used to define a repair policy which generates a goal-directed sequence of repair operators to face unplanned events and operational disturbances. An industrial example where rescheduling is needed due to the arrival of a new/rush order, or whenever raw material delay/shortage or machine breakdown events occur are discussed using the SmartGantt prototype for interactive rescheduling in real-time. SmartGantt demonstrates that due date compliance of orders-in-progress, negotiating delivery conditions of new orders and ensuring distributed production control can be dramatically improved by means of relational reinforcement learning and a deictic representation of rescheduling tasks.
Fil: Palombarini, Jorge Andrés. Universidad Tecnologica Nacional. Facultad Regional Villa Maria; Argentina
Fil: Martínez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina
Materia
Automated Planning
Information Systems
Manufacturing Systems
Real-Time Rescheduling
Reinforcement Learning
Relational Abstractions
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/70284
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/70284
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling SmartGantt - An intelligent system for real time rescheduling based on relational reinforcement learningPalombarini, Jorge AndrésMartínez, Ernesto CarlosAutomated PlanningInformation SystemsManufacturing SystemsReal-Time ReschedulingReinforcement LearningRelational Abstractionshttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2With the current trend towards cognitive manufacturing systems to deal with unforeseen events and disturbances that constantly demand real-time repair decisions, learning/reasoning skills and interactive capabilities are important functionalities for rescheduling a shop-floor on the fly taking into account several objectives and goal states. In this work, the automatic generation and update through learning of rescheduling knowledge using simulated transitions of abstract schedule states is proposed. Deictic representations of schedules based on focal points are used to define a repair policy which generates a goal-directed sequence of repair operators to face unplanned events and operational disturbances. An industrial example where rescheduling is needed due to the arrival of a new/rush order, or whenever raw material delay/shortage or machine breakdown events occur are discussed using the SmartGantt prototype for interactive rescheduling in real-time. SmartGantt demonstrates that due date compliance of orders-in-progress, negotiating delivery conditions of new orders and ensuring distributed production control can be dramatically improved by means of relational reinforcement learning and a deictic representation of rescheduling tasks.Fil: Palombarini, Jorge Andrés. Universidad Tecnologica Nacional. Facultad Regional Villa Maria; ArgentinaFil: Martínez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaElsevier Science Ltd2012-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/70284Palombarini, Jorge Andrés; Martínez, Ernesto Carlos; SmartGantt - An intelligent system for real time rescheduling based on relational reinforcement learning; Elsevier Science Ltd; Expert Systems with Applications; 39; 11; 9-2012; 10251-102680957-4174CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.eswa.2012.02.176info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:33:35Zoai:ri.conicet.gov.ar:11336/70284instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:33:35.273CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv SmartGantt - An intelligent system for real time rescheduling based on relational reinforcement learning
title SmartGantt - An intelligent system for real time rescheduling based on relational reinforcement learning
spellingShingle SmartGantt - An intelligent system for real time rescheduling based on relational reinforcement learning
Palombarini, Jorge Andrés
Automated Planning
Information Systems
Manufacturing Systems
Real-Time Rescheduling
Reinforcement Learning
Relational Abstractions
title_short SmartGantt - An intelligent system for real time rescheduling based on relational reinforcement learning
title_full SmartGantt - An intelligent system for real time rescheduling based on relational reinforcement learning
title_fullStr SmartGantt - An intelligent system for real time rescheduling based on relational reinforcement learning
title_full_unstemmed SmartGantt - An intelligent system for real time rescheduling based on relational reinforcement learning
title_sort SmartGantt - An intelligent system for real time rescheduling based on relational reinforcement learning
dc.creator.none.fl_str_mv Palombarini, Jorge Andrés
Martínez, Ernesto Carlos
author Palombarini, Jorge Andrés
author_facet Palombarini, Jorge Andrés
Martínez, Ernesto Carlos
author_role author
author2 Martínez, Ernesto Carlos
author2_role author
dc.subject.none.fl_str_mv Automated Planning
Information Systems
Manufacturing Systems
Real-Time Rescheduling
Reinforcement Learning
Relational Abstractions
topic Automated Planning
Information Systems
Manufacturing Systems
Real-Time Rescheduling
Reinforcement Learning
Relational Abstractions
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv With the current trend towards cognitive manufacturing systems to deal with unforeseen events and disturbances that constantly demand real-time repair decisions, learning/reasoning skills and interactive capabilities are important functionalities for rescheduling a shop-floor on the fly taking into account several objectives and goal states. In this work, the automatic generation and update through learning of rescheduling knowledge using simulated transitions of abstract schedule states is proposed. Deictic representations of schedules based on focal points are used to define a repair policy which generates a goal-directed sequence of repair operators to face unplanned events and operational disturbances. An industrial example where rescheduling is needed due to the arrival of a new/rush order, or whenever raw material delay/shortage or machine breakdown events occur are discussed using the SmartGantt prototype for interactive rescheduling in real-time. SmartGantt demonstrates that due date compliance of orders-in-progress, negotiating delivery conditions of new orders and ensuring distributed production control can be dramatically improved by means of relational reinforcement learning and a deictic representation of rescheduling tasks.
Fil: Palombarini, Jorge Andrés. Universidad Tecnologica Nacional. Facultad Regional Villa Maria; Argentina
Fil: Martínez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina
description With the current trend towards cognitive manufacturing systems to deal with unforeseen events and disturbances that constantly demand real-time repair decisions, learning/reasoning skills and interactive capabilities are important functionalities for rescheduling a shop-floor on the fly taking into account several objectives and goal states. In this work, the automatic generation and update through learning of rescheduling knowledge using simulated transitions of abstract schedule states is proposed. Deictic representations of schedules based on focal points are used to define a repair policy which generates a goal-directed sequence of repair operators to face unplanned events and operational disturbances. An industrial example where rescheduling is needed due to the arrival of a new/rush order, or whenever raw material delay/shortage or machine breakdown events occur are discussed using the SmartGantt prototype for interactive rescheduling in real-time. SmartGantt demonstrates that due date compliance of orders-in-progress, negotiating delivery conditions of new orders and ensuring distributed production control can be dramatically improved by means of relational reinforcement learning and a deictic representation of rescheduling tasks.
publishDate 2012
dc.date.none.fl_str_mv 2012-09
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/70284
Palombarini, Jorge Andrés; Martínez, Ernesto Carlos; SmartGantt - An intelligent system for real time rescheduling based on relational reinforcement learning; Elsevier Science Ltd; Expert Systems with Applications; 39; 11; 9-2012; 10251-10268
0957-4174
CONICET Digital
CONICET
url http://hdl.handle.net/11336/70284
identifier_str_mv Palombarini, Jorge Andrés; Martínez, Ernesto Carlos; SmartGantt - An intelligent system for real time rescheduling based on relational reinforcement learning; Elsevier Science Ltd; Expert Systems with Applications; 39; 11; 9-2012; 10251-10268
0957-4174
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.eswa.2012.02.176
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science Ltd
publisher.none.fl_str_mv Elsevier Science Ltd
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.069144

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