An ACO approach for the Parallel Machines Scheduling Problem

Autores
Gatica, Claudia R.; Esquivel, Susana Cecilia; Leguizamón, Guillermo
Año de publicación
2008
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
The parallel machines scheduling problem (Pm) comprises the allocation of jobs on the system’s resources, i.e., a group of machines in parallel. The basic model consists of m identical machines and n jobs. The jobs are assigned according to resource availability following some allocation rule. In this work, we apply the Ant Colony Optimization (ACO) metaheuristic which includes in the construction solution process different specific heuristic to solve Pm for the minimization Maximum Tardiness (Tmax). We also present a comparison of previous results obtained by a simple genetic algorithm (GAs) and an evidence of an improved performance of the ACO metaheuristic on this particular scheduling problem.
Workshop de Agentes y Sistemas Inteligentes (WASI)
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Parallel
Scheduling
Optimization
Heuristic methods
Ant Colony Optimization (ACO)
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:10915/21771
Acceder
id SEDICI_6a5514aaf3d59553ccafe715bdc3ba22
oai_identifier_str oai:sedici.unlp.edu.ar:10915/21771
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling An ACO approach for the Parallel Machines Scheduling ProblemGatica, Claudia R.Esquivel, Susana CeciliaLeguizamón, GuillermoCiencias InformáticasParallelSchedulingOptimizationHeuristic methodsAnt Colony Optimization (ACO)The parallel machines scheduling problem (Pm) comprises the allocation of jobs on the system’s resources, i.e., a group of machines in parallel. The basic model consists of m identical machines and n jobs. The jobs are assigned according to resource availability following some allocation rule. In this work, we apply the Ant Colony Optimization (ACO) metaheuristic which includes in the construction solution process different specific heuristic to solve P<sub>m</sub> for the minimization Maximum Tardiness (T<sub>max</sub>). We also present a comparison of previous results obtained by a simple genetic algorithm (GAs) and an evidence of an improved performance of the ACO metaheuristic on this particular scheduling problem.Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI)2008-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/21771enginfo: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:UNLP2025-10-15T10:47:23Zoai:sedici.unlp.edu.ar:10915/21771Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-15 10:47:24.131SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv An ACO approach for the Parallel Machines Scheduling Problem
title An ACO approach for the Parallel Machines Scheduling Problem
spellingShingle An ACO approach for the Parallel Machines Scheduling Problem
Gatica, Claudia R.
Ciencias Informáticas
Parallel
Scheduling
Optimization
Heuristic methods
Ant Colony Optimization (ACO)
title_short An ACO approach for the Parallel Machines Scheduling Problem
title_full An ACO approach for the Parallel Machines Scheduling Problem
title_fullStr An ACO approach for the Parallel Machines Scheduling Problem
title_full_unstemmed An ACO approach for the Parallel Machines Scheduling Problem
title_sort An ACO approach for the Parallel Machines Scheduling Problem
dc.creator.none.fl_str_mv Gatica, Claudia R.
Esquivel, Susana Cecilia
Leguizamón, Guillermo
author Gatica, Claudia R.
author_facet Gatica, Claudia R.
Esquivel, Susana Cecilia
Leguizamón, Guillermo
author_role author
author2 Esquivel, Susana Cecilia
Leguizamón, Guillermo
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Parallel
Scheduling
Optimization
Heuristic methods
Ant Colony Optimization (ACO)
topic Ciencias Informáticas
Parallel
Scheduling
Optimization
Heuristic methods
Ant Colony Optimization (ACO)
dc.description.none.fl_txt_mv The parallel machines scheduling problem (Pm) comprises the allocation of jobs on the system’s resources, i.e., a group of machines in parallel. The basic model consists of m identical machines and n jobs. The jobs are assigned according to resource availability following some allocation rule. In this work, we apply the Ant Colony Optimization (ACO) metaheuristic which includes in the construction solution process different specific heuristic to solve P<sub>m</sub> for the minimization Maximum Tardiness (T<sub>max</sub>). We also present a comparison of previous results obtained by a simple genetic algorithm (GAs) and an evidence of an improved performance of the ACO metaheuristic on this particular scheduling problem.
Workshop de Agentes y Sistemas Inteligentes (WASI)
Red de Universidades con Carreras en Informática (RedUNCI)
description The parallel machines scheduling problem (Pm) comprises the allocation of jobs on the system’s resources, i.e., a group of machines in parallel. The basic model consists of m identical machines and n jobs. The jobs are assigned according to resource availability following some allocation rule. In this work, we apply the Ant Colony Optimization (ACO) metaheuristic which includes in the construction solution process different specific heuristic to solve P<sub>m</sub> for the minimization Maximum Tardiness (T<sub>max</sub>). We also present a comparison of previous results obtained by a simple genetic algorithm (GAs) and an evidence of an improved performance of the ACO metaheuristic on this particular scheduling problem.
publishDate 2008
dc.date.none.fl_str_mv 2008-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/21771
url http://sedici.unlp.edu.ar/handle/10915/21771
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
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
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
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
_version_ 1846063899758034944
score 13.22299

Publicaciones similares