Evolutionary optimization of due date based objectives in unrestricted identical parallel machine scheduling problems

Autores
Ferretti, Edgardo; Esquivel, Susana Cecilia; Gallard, Raúl Hector
Año de publicación
2004
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Parallel machine scheduling, involves the allocation of jobs to the system resources (a bank of machines in parallel). A basic model consisting of m machines and n jobs is the foundation of more complex models. Here, jobs are allocated according to resource availability following some allocation rule. In the specialised literature, minimisation of the makespan has been extensively approached and benchmarks can be easily found. This is not the case for other important objectives such as the maximum tardiness and the number of tardy jobs. These problems are NP-hard for 2 ≤ m ≤ n, and conventional heuristics and evolutionary algorithms (EAs) have been developed to provide acceptable schedules as solutions. To solve the unrestricted identical parallel machine scheduling problems, this paper proposes MCMP-SRI and MCMP-SRSI, which are two multirecombination schemes that combine studs, random and seed immigrants. Evidence of the improved behaviour of the EAs when inserting problem-specific knowledge is provided. Experiments and results are discussed.
Eje: V - Workshop de agentes y sistemas inteligentes
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Parallel machine scheduling
evolutionary algorithms
multirecombination
maximum tardiness
number of tardy jobs
Parallel
Scheduling
Algorithms
ARTIFICIAL INTELLIGENCE
Intelligent agents
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:10915/22554
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/22554
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repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Evolutionary optimization of due date based objectives in unrestricted identical parallel machine scheduling problemsFerretti, EdgardoEsquivel, Susana CeciliaGallard, Raúl HectorCiencias InformáticasParallel machine schedulingevolutionary algorithmsmultirecombinationmaximum tardinessnumber of tardy jobsParallelSchedulingAlgorithmsARTIFICIAL INTELLIGENCEIntelligent agentsParallel machine scheduling, involves the allocation of jobs to the system resources (a bank of machines in parallel). A basic model consisting of m machines and n jobs is the foundation of more complex models. Here, jobs are allocated according to resource availability following some allocation rule. In the specialised literature, minimisation of the makespan has been extensively approached and benchmarks can be easily found. This is not the case for other important objectives such as the maximum tardiness and the number of tardy jobs. These problems are NP-hard for 2 ≤ m ≤ n, and conventional heuristics and evolutionary algorithms (EAs) have been developed to provide acceptable schedules as solutions. To solve the unrestricted identical parallel machine scheduling problems, this paper proposes MCMP-SRI and MCMP-SRSI, which are two multirecombination schemes that combine studs, random and seed immigrants. Evidence of the improved behaviour of the EAs when inserting problem-specific knowledge is provided. Experiments and results are discussed.Eje: V - Workshop de agentes y sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI)2004-10info: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/10915/22554enginfo: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:UNLP2025-09-29T10:55:06Zoai:sedici.unlp.edu.ar:10915/22554Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:55:07.209SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Evolutionary optimization of due date based objectives in unrestricted identical parallel machine scheduling problems
title Evolutionary optimization of due date based objectives in unrestricted identical parallel machine scheduling problems
spellingShingle Evolutionary optimization of due date based objectives in unrestricted identical parallel machine scheduling problems
Ferretti, Edgardo
Ciencias Informáticas
Parallel machine scheduling
evolutionary algorithms
multirecombination
maximum tardiness
number of tardy jobs
Parallel
Scheduling
Algorithms
ARTIFICIAL INTELLIGENCE
Intelligent agents
title_short Evolutionary optimization of due date based objectives in unrestricted identical parallel machine scheduling problems
title_full Evolutionary optimization of due date based objectives in unrestricted identical parallel machine scheduling problems
title_fullStr Evolutionary optimization of due date based objectives in unrestricted identical parallel machine scheduling problems
title_full_unstemmed Evolutionary optimization of due date based objectives in unrestricted identical parallel machine scheduling problems
title_sort Evolutionary optimization of due date based objectives in unrestricted identical parallel machine scheduling problems
dc.creator.none.fl_str_mv Ferretti, Edgardo
Esquivel, Susana Cecilia
Gallard, Raúl Hector
author Ferretti, Edgardo
author_facet Ferretti, Edgardo
Esquivel, Susana Cecilia
Gallard, Raúl Hector
author_role author
author2 Esquivel, Susana Cecilia
Gallard, Raúl Hector
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Parallel machine scheduling
evolutionary algorithms
multirecombination
maximum tardiness
number of tardy jobs
Parallel
Scheduling
Algorithms
ARTIFICIAL INTELLIGENCE
Intelligent agents
topic Ciencias Informáticas
Parallel machine scheduling
evolutionary algorithms
multirecombination
maximum tardiness
number of tardy jobs
Parallel
Scheduling
Algorithms
ARTIFICIAL INTELLIGENCE
Intelligent agents
dc.description.none.fl_txt_mv Parallel machine scheduling, involves the allocation of jobs to the system resources (a bank of machines in parallel). A basic model consisting of m machines and n jobs is the foundation of more complex models. Here, jobs are allocated according to resource availability following some allocation rule. In the specialised literature, minimisation of the makespan has been extensively approached and benchmarks can be easily found. This is not the case for other important objectives such as the maximum tardiness and the number of tardy jobs. These problems are NP-hard for 2 ≤ m ≤ n, and conventional heuristics and evolutionary algorithms (EAs) have been developed to provide acceptable schedules as solutions. To solve the unrestricted identical parallel machine scheduling problems, this paper proposes MCMP-SRI and MCMP-SRSI, which are two multirecombination schemes that combine studs, random and seed immigrants. Evidence of the improved behaviour of the EAs when inserting problem-specific knowledge is provided. Experiments and results are discussed.
Eje: V - Workshop de agentes y sistemas inteligentes
Red de Universidades con Carreras en Informática (RedUNCI)
description Parallel machine scheduling, involves the allocation of jobs to the system resources (a bank of machines in parallel). A basic model consisting of m machines and n jobs is the foundation of more complex models. Here, jobs are allocated according to resource availability following some allocation rule. In the specialised literature, minimisation of the makespan has been extensively approached and benchmarks can be easily found. This is not the case for other important objectives such as the maximum tardiness and the number of tardy jobs. These problems are NP-hard for 2 ≤ m ≤ n, and conventional heuristics and evolutionary algorithms (EAs) have been developed to provide acceptable schedules as solutions. To solve the unrestricted identical parallel machine scheduling problems, this paper proposes MCMP-SRI and MCMP-SRSI, which are two multirecombination schemes that combine studs, random and seed immigrants. Evidence of the improved behaviour of the EAs when inserting problem-specific knowledge is provided. Experiments and results are discussed.
publishDate 2004
dc.date.none.fl_str_mv 2004-10
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/10915/22554
url http://sedici.unlp.edu.ar/handle/10915/22554
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
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reponame_str SEDICI (UNLP)
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instname_str Universidad Nacional de La Plata
instacron_str UNLP
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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