An ACO approach for the parallel machines scheduling problem

Autores
Gatica, Claudia Ruth; Esquivel, Susana Cecilia; Leguizamon, Mario Guillermo
Año de publicación
2010
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The parallel machines scheduling problem (PMSP) comprises the allocation of jobs on the resources of the systems, 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 four different specific heuristics in the solution construction process to solve unrestricted PMSP for the minimization of the Maximum Tardiness (Tmax) objective. 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.
Fil: Gatica, Claudia Ruth. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis; Argentina. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina
Fil: Esquivel, Susana Cecilia. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina
Fil: Leguizamon, Mario Guillermo. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina
Materia
PARALLEL MACHINE SCHEDULING
MAXIMUM TARDINESS
ANT COLONY OPTIMIZATION ALGORITHMS
SPECIFIC HEURISTIC PROBLEM INFORMATION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/158911
Acceder
id CONICETDig_a2f1658fb47195fd7f6aefe7f33d3048
oai_identifier_str oai:ri.conicet.gov.ar:11336/158911
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling An ACO approach for the parallel machines scheduling problemGatica, Claudia RuthEsquivel, Susana CeciliaLeguizamon, Mario GuillermoPARALLEL MACHINE SCHEDULINGMAXIMUM TARDINESSANT COLONY OPTIMIZATION ALGORITHMSSPECIFIC HEURISTIC PROBLEM INFORMATIONhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1The parallel machines scheduling problem (PMSP) comprises the allocation of jobs on the resources of the systems, 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 four different specific heuristics in the solution construction process to solve unrestricted PMSP for the minimization of the Maximum Tardiness (Tmax) objective. 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.Fil: Gatica, Claudia Ruth. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis; Argentina. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; ArgentinaFil: Esquivel, Susana Cecilia. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; ArgentinaFil: Leguizamon, Mario Guillermo. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; ArgentinaSociedad Iberoamericana de Inteligencia Artificial2010-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/158911Gatica, Claudia Ruth; Esquivel, Susana Cecilia; Leguizamon, Mario Guillermo; An ACO approach for the parallel machines scheduling problem; Sociedad Iberoamericana de Inteligencia Artificial; Inteligencia Artificial; 14; 46; 6-2010; 84-951137-36011988-3064CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://journal.iberamia.org/public/Vol.1-14.htmlinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:43:09Zoai:ri.conicet.gov.ar:11336/158911instacron: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 10:43:09.811CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
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 Ruth
PARALLEL MACHINE SCHEDULING
MAXIMUM TARDINESS
ANT COLONY OPTIMIZATION ALGORITHMS
SPECIFIC HEURISTIC PROBLEM INFORMATION
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 Ruth
Esquivel, Susana Cecilia
Leguizamon, Mario Guillermo
author Gatica, Claudia Ruth
author_facet Gatica, Claudia Ruth
Esquivel, Susana Cecilia
Leguizamon, Mario Guillermo
author_role author
author2 Esquivel, Susana Cecilia
Leguizamon, Mario Guillermo
author2_role author
author
dc.subject.none.fl_str_mv PARALLEL MACHINE SCHEDULING
MAXIMUM TARDINESS
ANT COLONY OPTIMIZATION ALGORITHMS
SPECIFIC HEURISTIC PROBLEM INFORMATION
topic PARALLEL MACHINE SCHEDULING
MAXIMUM TARDINESS
ANT COLONY OPTIMIZATION ALGORITHMS
SPECIFIC HEURISTIC PROBLEM INFORMATION
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The parallel machines scheduling problem (PMSP) comprises the allocation of jobs on the resources of the systems, 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 four different specific heuristics in the solution construction process to solve unrestricted PMSP for the minimization of the Maximum Tardiness (Tmax) objective. 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.
Fil: Gatica, Claudia Ruth. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis; Argentina. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina
Fil: Esquivel, Susana Cecilia. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina
Fil: Leguizamon, Mario Guillermo. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina
description The parallel machines scheduling problem (PMSP) comprises the allocation of jobs on the resources of the systems, 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 four different specific heuristics in the solution construction process to solve unrestricted PMSP for the minimization of the Maximum Tardiness (Tmax) objective. 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 2010
dc.date.none.fl_str_mv 2010-06
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/11336/158911
Gatica, Claudia Ruth; Esquivel, Susana Cecilia; Leguizamon, Mario Guillermo; An ACO approach for the parallel machines scheduling problem; Sociedad Iberoamericana de Inteligencia Artificial; Inteligencia Artificial; 14; 46; 6-2010; 84-95
1137-3601
1988-3064
CONICET Digital
CONICET
url http://hdl.handle.net/11336/158911
identifier_str_mv Gatica, Claudia Ruth; Esquivel, Susana Cecilia; Leguizamon, Mario Guillermo; An ACO approach for the parallel machines scheduling problem; Sociedad Iberoamericana de Inteligencia Artificial; Inteligencia Artificial; 14; 46; 6-2010; 84-95
1137-3601
1988-3064
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.iberamia.org/public/Vol.1-14.html
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Sociedad Iberoamericana de Inteligencia Artificial
publisher.none.fl_str_mv Sociedad Iberoamericana de Inteligencia Artificial
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
_version_ 1844614466192277504
score 13.070432

Publicaciones similares