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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/22554
Ver los metadatos del registro completo
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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:UNLP2025-09-03T10:27:53Zoai: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:13292025-09-03 10:27:53.88SEDICI (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 |
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publishedVersion |
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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) |
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openAccess |
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http://creativecommons.org/licenses/by-nc-sa/2.5/ar/ Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) |
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