Multi-agent Learning by Trial and Error for Resource Leveling during Multi-Project (Re)scheduling

Autores
Tosselli, Laura; Bogado, Verónica Soledad; Martínez, Ernesto Carlos
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In a multi-project context within enterprise networks, reaching feasible solutions to the (re)scheduling problem represents a major challenge, mainly when scarce resources are shared among projects. The multi-project (re)scheduling must achieve the most efficient possible resource usage without increasing the prescribed project constraints, considering the Resource Leveling Problem (RLP), whose objective is to level the consumption of resources shared in order to minimize their idle times and to avoid overallocation conflicts. In this work, a multi-agent solution that allows solving the Resource Constrained Multi-project Scheduling Problem (RCMPSP) and the Resource Investment Problem is extended to incorporate indicators on agents? payoff functions to address the Resource Leveling Problem in a decentralized and autonomous way, through decoupled rules based on Trial-and-Error approach. The proposed agent-based simulation model is tested through a set of project instances that vary in their structure, parameters, number of resources shared, etc. Results obtained are assessed through different scheduling goals, such as project total duration, project total cost and leveling resource usage. Our results are far better compared to the ones obtained with alternative approaches. This proposal shows that the interacting agents that implement decoupled learning rules find a solution which can be understood as a Nash equilibrium.
Fil: Tosselli, Laura. Universidad Tecnológica Nacional; Argentina
Fil: Bogado, Verónica Soledad. Universidad Tecnológica Nacional; 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
MULTI-AGENT SYSTEMS
FRACTAL ORGANIZATIONS
PROJECT-BASED MANAGEMENT
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:11336/87145
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Multi-agent Learning by Trial and Error for Resource Leveling during Multi-Project (Re)schedulingTosselli, LauraBogado, Verónica SoledadMartínez, Ernesto CarlosAGENT-BASED SIMULATIONMULTI-AGENT SYSTEMSFRACTAL ORGANIZATIONSPROJECT-BASED MANAGEMENThttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2In a multi-project context within enterprise networks, reaching feasible solutions to the (re)scheduling problem represents a major challenge, mainly when scarce resources are shared among projects. The multi-project (re)scheduling must achieve the most efficient possible resource usage without increasing the prescribed project constraints, considering the Resource Leveling Problem (RLP), whose objective is to level the consumption of resources shared in order to minimize their idle times and to avoid overallocation conflicts. In this work, a multi-agent solution that allows solving the Resource Constrained Multi-project Scheduling Problem (RCMPSP) and the Resource Investment Problem is extended to incorporate indicators on agents? payoff functions to address the Resource Leveling Problem in a decentralized and autonomous way, through decoupled rules based on Trial-and-Error approach. The proposed agent-based simulation model is tested through a set of project instances that vary in their structure, parameters, number of resources shared, etc. Results obtained are assessed through different scheduling goals, such as project total duration, project total cost and leveling resource usage. Our results are far better compared to the ones obtained with alternative approaches. This proposal shows that the interacting agents that implement decoupled learning rules find a solution which can be understood as a Nash equilibrium.Fil: Tosselli, Laura. Universidad Tecnológica Nacional; ArgentinaFil: Bogado, Verónica Soledad. Universidad Tecnológica Nacional; 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; ArgentinaRedUNCI2018-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/87145Tosselli, Laura; Bogado, Verónica Soledad; Martínez, Ernesto Carlos; Multi-agent Learning by Trial and Error for Resource Leveling during Multi-Project (Re)scheduling; RedUNCI; Journal of Computer Science & Technology; 18; 2; 10-2018; 125-1351666-6038CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/JCST/article/view/1085info:eu-repo/semantics/altIdentifier/doi/10.24215/16666038.18.e14info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:21:58Zoai:ri.conicet.gov.ar:11336/87145instacron: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 10:21:58.867CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Multi-agent Learning by Trial and Error for Resource Leveling during Multi-Project (Re)scheduling
title Multi-agent Learning by Trial and Error for Resource Leveling during Multi-Project (Re)scheduling
spellingShingle Multi-agent Learning by Trial and Error for Resource Leveling during Multi-Project (Re)scheduling
Tosselli, Laura
AGENT-BASED SIMULATION
MULTI-AGENT SYSTEMS
FRACTAL ORGANIZATIONS
PROJECT-BASED MANAGEMENT
title_short Multi-agent Learning by Trial and Error for Resource Leveling during Multi-Project (Re)scheduling
title_full Multi-agent Learning by Trial and Error for Resource Leveling during Multi-Project (Re)scheduling
title_fullStr Multi-agent Learning by Trial and Error for Resource Leveling during Multi-Project (Re)scheduling
title_full_unstemmed Multi-agent Learning by Trial and Error for Resource Leveling during Multi-Project (Re)scheduling
title_sort Multi-agent Learning by Trial and Error for Resource Leveling during Multi-Project (Re)scheduling
dc.creator.none.fl_str_mv Tosselli, Laura
Bogado, Verónica Soledad
Martínez, Ernesto Carlos
author Tosselli, Laura
author_facet Tosselli, Laura
Bogado, Verónica Soledad
Martínez, Ernesto Carlos
author_role author
author2 Bogado, Verónica Soledad
Martínez, Ernesto Carlos
author2_role author
author
dc.subject.none.fl_str_mv AGENT-BASED SIMULATION
MULTI-AGENT SYSTEMS
FRACTAL ORGANIZATIONS
PROJECT-BASED MANAGEMENT
topic AGENT-BASED SIMULATION
MULTI-AGENT SYSTEMS
FRACTAL ORGANIZATIONS
PROJECT-BASED MANAGEMENT
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 a multi-project context within enterprise networks, reaching feasible solutions to the (re)scheduling problem represents a major challenge, mainly when scarce resources are shared among projects. The multi-project (re)scheduling must achieve the most efficient possible resource usage without increasing the prescribed project constraints, considering the Resource Leveling Problem (RLP), whose objective is to level the consumption of resources shared in order to minimize their idle times and to avoid overallocation conflicts. In this work, a multi-agent solution that allows solving the Resource Constrained Multi-project Scheduling Problem (RCMPSP) and the Resource Investment Problem is extended to incorporate indicators on agents? payoff functions to address the Resource Leveling Problem in a decentralized and autonomous way, through decoupled rules based on Trial-and-Error approach. The proposed agent-based simulation model is tested through a set of project instances that vary in their structure, parameters, number of resources shared, etc. Results obtained are assessed through different scheduling goals, such as project total duration, project total cost and leveling resource usage. Our results are far better compared to the ones obtained with alternative approaches. This proposal shows that the interacting agents that implement decoupled learning rules find a solution which can be understood as a Nash equilibrium.
Fil: Tosselli, Laura. Universidad Tecnológica Nacional; Argentina
Fil: Bogado, Verónica Soledad. Universidad Tecnológica Nacional; 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 a multi-project context within enterprise networks, reaching feasible solutions to the (re)scheduling problem represents a major challenge, mainly when scarce resources are shared among projects. The multi-project (re)scheduling must achieve the most efficient possible resource usage without increasing the prescribed project constraints, considering the Resource Leveling Problem (RLP), whose objective is to level the consumption of resources shared in order to minimize their idle times and to avoid overallocation conflicts. In this work, a multi-agent solution that allows solving the Resource Constrained Multi-project Scheduling Problem (RCMPSP) and the Resource Investment Problem is extended to incorporate indicators on agents? payoff functions to address the Resource Leveling Problem in a decentralized and autonomous way, through decoupled rules based on Trial-and-Error approach. The proposed agent-based simulation model is tested through a set of project instances that vary in their structure, parameters, number of resources shared, etc. Results obtained are assessed through different scheduling goals, such as project total duration, project total cost and leveling resource usage. Our results are far better compared to the ones obtained with alternative approaches. This proposal shows that the interacting agents that implement decoupled learning rules find a solution which can be understood as a Nash equilibrium.
publishDate 2018
dc.date.none.fl_str_mv 2018-10
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/11336/87145
Tosselli, Laura; Bogado, Verónica Soledad; Martínez, Ernesto Carlos; Multi-agent Learning by Trial and Error for Resource Leveling during Multi-Project (Re)scheduling; RedUNCI; Journal of Computer Science & Technology; 18; 2; 10-2018; 125-135
1666-6038
CONICET Digital
CONICET
url http://hdl.handle.net/11336/87145
identifier_str_mv Tosselli, Laura; Bogado, Verónica Soledad; Martínez, Ernesto Carlos; Multi-agent Learning by Trial and Error for Resource Leveling during Multi-Project (Re)scheduling; RedUNCI; Journal of Computer Science & Technology; 18; 2; 10-2018; 125-135
1666-6038
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/JCST/article/view/1085
info:eu-repo/semantics/altIdentifier/doi/10.24215/16666038.18.e14
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 RedUNCI
publisher.none.fl_str_mv RedUNCI
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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