Agent learning in autonomic manufacturing execution systems for enterprise networking

Autores
Rolon, Maria de Los Milagros; Martínez, Ernesto Carlos
Año de publicación
2012
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In enterprise networks, companies interact on a temporal basis through client-server relationships between order agents (clients) and resource agents (servers) acting as autonomic managers. In this work, the autonomic MES (@MES) proposed by Rolón and Martinez (2012) has been extended to allow selfish behavior and adaptive decision-making in distributed execution control and emergent scheduling. Agent learning in the @MES is addressed by rewarding order agents in order to continuously optimize their processing routes based on cost and reliability of alternative resource agents (servers). Service providers are rewarded so as to learn the quality level corresponding to each task which is used to define the processing time and cost for each client request. Two reinforcement learning algorithms have been implemented to simulate learning curves of client-server relationships in the @MES. Emerging behaviors obtained through generative simulation in a case study show that despite selfish behavior and policy adaptation in order and resource agents, the autonomic MES is able to reject significant disturbances and handle unplanned events successfully.
Fil: Rolon, Maria de Los Milagros. 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
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
Agent-Based Simulation
Autonomic Systems
Distributed Production Control
Enterprise Networking
Manufacturing Execution Systems
Multi-Agent Learning
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:11336/70200
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/70200
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Agent learning in autonomic manufacturing execution systems for enterprise networkingRolon, Maria de Los MilagrosMartínez, Ernesto CarlosAgent-Based SimulationAutonomic SystemsDistributed Production ControlEnterprise NetworkingManufacturing Execution SystemsMulti-Agent Learninghttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2In enterprise networks, companies interact on a temporal basis through client-server relationships between order agents (clients) and resource agents (servers) acting as autonomic managers. In this work, the autonomic MES (@MES) proposed by Rolón and Martinez (2012) has been extended to allow selfish behavior and adaptive decision-making in distributed execution control and emergent scheduling. Agent learning in the @MES is addressed by rewarding order agents in order to continuously optimize their processing routes based on cost and reliability of alternative resource agents (servers). Service providers are rewarded so as to learn the quality level corresponding to each task which is used to define the processing time and cost for each client request. Two reinforcement learning algorithms have been implemented to simulate learning curves of client-server relationships in the @MES. Emerging behaviors obtained through generative simulation in a case study show that despite selfish behavior and policy adaptation in order and resource agents, the autonomic MES is able to reject significant disturbances and handle unplanned events successfully.Fil: Rolon, Maria de Los Milagros. 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; 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; ArgentinaPergamon-Elsevier Science Ltd2012-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/70200Rolon, Maria de Los Milagros; Martínez, Ernesto Carlos; Agent learning in autonomic manufacturing execution systems for enterprise networking; Pergamon-Elsevier Science Ltd; Computers & Industrial Engineering; 63; 4; 12-2012; 901-9250360-8352CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.cie.2012.06.004info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:41:05Zoai:ri.conicet.gov.ar:11336/70200instacron: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 10:41:05.531CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Agent learning in autonomic manufacturing execution systems for enterprise networking
title Agent learning in autonomic manufacturing execution systems for enterprise networking
spellingShingle Agent learning in autonomic manufacturing execution systems for enterprise networking
Rolon, Maria de Los Milagros
Agent-Based Simulation
Autonomic Systems
Distributed Production Control
Enterprise Networking
Manufacturing Execution Systems
Multi-Agent Learning
title_short Agent learning in autonomic manufacturing execution systems for enterprise networking
title_full Agent learning in autonomic manufacturing execution systems for enterprise networking
title_fullStr Agent learning in autonomic manufacturing execution systems for enterprise networking
title_full_unstemmed Agent learning in autonomic manufacturing execution systems for enterprise networking
title_sort Agent learning in autonomic manufacturing execution systems for enterprise networking
dc.creator.none.fl_str_mv Rolon, Maria de Los Milagros
Martínez, Ernesto Carlos
author Rolon, Maria de Los Milagros
author_facet Rolon, Maria de Los Milagros
Martínez, Ernesto Carlos
author_role author
author2 Martínez, Ernesto Carlos
author2_role author
dc.subject.none.fl_str_mv Agent-Based Simulation
Autonomic Systems
Distributed Production Control
Enterprise Networking
Manufacturing Execution Systems
Multi-Agent Learning
topic Agent-Based Simulation
Autonomic Systems
Distributed Production Control
Enterprise Networking
Manufacturing Execution Systems
Multi-Agent Learning
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv In enterprise networks, companies interact on a temporal basis through client-server relationships between order agents (clients) and resource agents (servers) acting as autonomic managers. In this work, the autonomic MES (@MES) proposed by Rolón and Martinez (2012) has been extended to allow selfish behavior and adaptive decision-making in distributed execution control and emergent scheduling. Agent learning in the @MES is addressed by rewarding order agents in order to continuously optimize their processing routes based on cost and reliability of alternative resource agents (servers). Service providers are rewarded so as to learn the quality level corresponding to each task which is used to define the processing time and cost for each client request. Two reinforcement learning algorithms have been implemented to simulate learning curves of client-server relationships in the @MES. Emerging behaviors obtained through generative simulation in a case study show that despite selfish behavior and policy adaptation in order and resource agents, the autonomic MES is able to reject significant disturbances and handle unplanned events successfully.
Fil: Rolon, Maria de Los Milagros. 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
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 In enterprise networks, companies interact on a temporal basis through client-server relationships between order agents (clients) and resource agents (servers) acting as autonomic managers. In this work, the autonomic MES (@MES) proposed by Rolón and Martinez (2012) has been extended to allow selfish behavior and adaptive decision-making in distributed execution control and emergent scheduling. Agent learning in the @MES is addressed by rewarding order agents in order to continuously optimize their processing routes based on cost and reliability of alternative resource agents (servers). Service providers are rewarded so as to learn the quality level corresponding to each task which is used to define the processing time and cost for each client request. Two reinforcement learning algorithms have been implemented to simulate learning curves of client-server relationships in the @MES. Emerging behaviors obtained through generative simulation in a case study show that despite selfish behavior and policy adaptation in order and resource agents, the autonomic MES is able to reject significant disturbances and handle unplanned events successfully.
publishDate 2012
dc.date.none.fl_str_mv 2012-12
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/11336/70200
Rolon, Maria de Los Milagros; Martínez, Ernesto Carlos; Agent learning in autonomic manufacturing execution systems for enterprise networking; Pergamon-Elsevier Science Ltd; Computers & Industrial Engineering; 63; 4; 12-2012; 901-925
0360-8352
CONICET Digital
CONICET
url http://hdl.handle.net/11336/70200
identifier_str_mv Rolon, Maria de Los Milagros; Martínez, Ernesto Carlos; Agent learning in autonomic manufacturing execution systems for enterprise networking; Pergamon-Elsevier Science Ltd; Computers & Industrial Engineering; 63; 4; 12-2012; 901-925
0360-8352
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.cie.2012.06.004
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
dc.publisher.none.fl_str_mv Pergamon-Elsevier Science Ltd
publisher.none.fl_str_mv Pergamon-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 13.070432

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