A comparison between centralized and decentralized genetic algorithms for the identical parallel machines scheduling

Autores
Esquivel, Susana Cecilia
Año de publicación
2006
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Identical parallel machines problems (Pm) involve task assignments to the system's resources (a machine bank in parallel). The basic model consists of m machines and n tasks. The tasks are assigned according to the availability of the resources, following some allocation rule. In this work, the minimization of some objectives related to the due dates such as the maximum tardiness (Tmax) and the average tardiness (Tavg) were dealt with centralized and decentralized evolutive algorithms (EAs). In order to test our algorithms we used standard benchmarks. The main goal of this research was determinate the quality of the results obtained with a centralized GA and three decentralized GAs used to solve parallel machines scheduling problems. The results were compared using the ANOVA statistic method.
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Scheduling
Parallel
Algorithms
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/22620
Acceder
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network_name_str SEDICI (UNLP)
spelling A comparison between centralized and decentralized genetic algorithms for the identical parallel machines schedulingEsquivel, Susana CeciliaCiencias InformáticasSchedulingParallelAlgorithmsIdentical parallel machines problems (Pm) involve task assignments to the system's resources (a machine bank in parallel). The basic model consists of m machines and n tasks. The tasks are assigned according to the availability of the resources, following some allocation rule. In this work, the minimization of some objectives related to the due dates such as the maximum tardiness (Tmax) and the average tardiness (Tavg) were dealt with centralized and decentralized evolutive algorithms (EAs). In order to test our algorithms we used standard benchmarks. The main goal of this research was determinate the quality of the results obtained with a centralized GA and three decentralized GAs used to solve parallel machines scheduling problems. The results were compared using the ANOVA statistic method.Red de Universidades con Carreras en Informática (RedUNCI)2006-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf1118-1125http://sedici.unlp.edu.ar/handle/10915/22620enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T10:55:07Zoai:sedici.unlp.edu.ar:10915/22620Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:55:07.391SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv A comparison between centralized and decentralized genetic algorithms for the identical parallel machines scheduling
title A comparison between centralized and decentralized genetic algorithms for the identical parallel machines scheduling
spellingShingle A comparison between centralized and decentralized genetic algorithms for the identical parallel machines scheduling
Esquivel, Susana Cecilia
Ciencias Informáticas
Scheduling
Parallel
Algorithms
title_short A comparison between centralized and decentralized genetic algorithms for the identical parallel machines scheduling
title_full A comparison between centralized and decentralized genetic algorithms for the identical parallel machines scheduling
title_fullStr A comparison between centralized and decentralized genetic algorithms for the identical parallel machines scheduling
title_full_unstemmed A comparison between centralized and decentralized genetic algorithms for the identical parallel machines scheduling
title_sort A comparison between centralized and decentralized genetic algorithms for the identical parallel machines scheduling
dc.creator.none.fl_str_mv Esquivel, Susana Cecilia
author Esquivel, Susana Cecilia
author_facet Esquivel, Susana Cecilia
author_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Scheduling
Parallel
Algorithms
topic Ciencias Informáticas
Scheduling
Parallel
Algorithms
dc.description.none.fl_txt_mv Identical parallel machines problems (Pm) involve task assignments to the system's resources (a machine bank in parallel). The basic model consists of m machines and n tasks. The tasks are assigned according to the availability of the resources, following some allocation rule. In this work, the minimization of some objectives related to the due dates such as the maximum tardiness (Tmax) and the average tardiness (Tavg) were dealt with centralized and decentralized evolutive algorithms (EAs). In order to test our algorithms we used standard benchmarks. The main goal of this research was determinate the quality of the results obtained with a centralized GA and three decentralized GAs used to solve parallel machines scheduling problems. The results were compared using the ANOVA statistic method.
Red de Universidades con Carreras en Informática (RedUNCI)
description Identical parallel machines problems (Pm) involve task assignments to the system's resources (a machine bank in parallel). The basic model consists of m machines and n tasks. The tasks are assigned according to the availability of the resources, following some allocation rule. In this work, the minimization of some objectives related to the due dates such as the maximum tardiness (Tmax) and the average tardiness (Tavg) were dealt with centralized and decentralized evolutive algorithms (EAs). In order to test our algorithms we used standard benchmarks. The main goal of this research was determinate the quality of the results obtained with a centralized GA and three decentralized GAs used to solve parallel machines scheduling problems. The results were compared using the ANOVA statistic method.
publishDate 2006
dc.date.none.fl_str_mv 2006-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
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info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/22620
url http://sedici.unlp.edu.ar/handle/10915/22620
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
1118-1125
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instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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