Multiple crossovers on multiple parents for the multiobjective flow shop problem
- Autores
- Esquivel, Susana Cecilia; Zuppa, Federico; Gallard, Raúl Hector
- Año de publicación
- 2002
- Idioma
- español castellano
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- The Flow Shop Scheduling Problem have been tackled using different techniques which goes from mathematical techniques like Branch and Bound to metaheuristics like evolutionary algorithms (EAs). Although in the real world this problem will be found more frequently with more than one objective, most work been done is based on a single objective. Evolutionary algorithms are very promising in this area because the outcome of a multiobjective problem is a set of optimal solutions (the Pareto Front) which EAs can provide in a single run. Yet another advantage of EA’s over other techniques is that they are less liable to the shape or continuity of the Pareto Front. In this work, we show three implementations of multiobjective Evolutionary Algorithms. The first one uses Single Crossover Per Couple (SCPC), while the other two use Multiple Crossover on Multiple Parents (MCMP), continuing with previous works[7, 8]. These two methods show an enhancement on the performance of the first method. Details of implementation and results are discussed.
Eje: Sistemas inteligentes
Red de Universidades con Carreras en Informática (RedUNCI) - Materia
-
Ciencias Informáticas
Scheduling
Evolutionary Computation
Flow shop scheduling
Optimization
multiobjective optimization
ARTIFICIAL INTELLIGENCE
multirecombination - 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:10915/23124
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Multiple crossovers on multiple parents for the multiobjective flow shop problemEsquivel, Susana CeciliaZuppa, FedericoGallard, Raúl HectorCiencias InformáticasSchedulingEvolutionary ComputationFlow shop schedulingOptimizationmultiobjective optimizationARTIFICIAL INTELLIGENCEmultirecombinationThe Flow Shop Scheduling Problem have been tackled using different techniques which goes from mathematical techniques like Branch and Bound to metaheuristics like evolutionary algorithms (EAs). Although in the real world this problem will be found more frequently with more than one objective, most work been done is based on a single objective. Evolutionary algorithms are very promising in this area because the outcome of a multiobjective problem is a set of optimal solutions (the Pareto Front) which EAs can provide in a single run. Yet another advantage of EA’s over other techniques is that they are less liable to the shape or continuity of the Pareto Front. In this work, we show three implementations of multiobjective Evolutionary Algorithms. The first one uses Single Crossover Per Couple (SCPC), while the other two use Multiple Crossover on Multiple Parents (MCMP), continuing with previous works[7, 8]. These two methods show an enhancement on the performance of the first method. Details of implementation and results are discussed.Eje: Sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI)2002-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf401-410http://sedici.unlp.edu.ar/handle/10915/23124spainfo: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:UNLP2025-09-03T10:28:07Zoai:sedici.unlp.edu.ar:10915/23124Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 10:28:08.128SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
Multiple crossovers on multiple parents for the multiobjective flow shop problem |
title |
Multiple crossovers on multiple parents for the multiobjective flow shop problem |
spellingShingle |
Multiple crossovers on multiple parents for the multiobjective flow shop problem Esquivel, Susana Cecilia Ciencias Informáticas Scheduling Evolutionary Computation Flow shop scheduling Optimization multiobjective optimization ARTIFICIAL INTELLIGENCE multirecombination |
title_short |
Multiple crossovers on multiple parents for the multiobjective flow shop problem |
title_full |
Multiple crossovers on multiple parents for the multiobjective flow shop problem |
title_fullStr |
Multiple crossovers on multiple parents for the multiobjective flow shop problem |
title_full_unstemmed |
Multiple crossovers on multiple parents for the multiobjective flow shop problem |
title_sort |
Multiple crossovers on multiple parents for the multiobjective flow shop problem |
dc.creator.none.fl_str_mv |
Esquivel, Susana Cecilia Zuppa, Federico Gallard, Raúl Hector |
author |
Esquivel, Susana Cecilia |
author_facet |
Esquivel, Susana Cecilia Zuppa, Federico Gallard, Raúl Hector |
author_role |
author |
author2 |
Zuppa, Federico Gallard, Raúl Hector |
author2_role |
author author |
dc.subject.none.fl_str_mv |
Ciencias Informáticas Scheduling Evolutionary Computation Flow shop scheduling Optimization multiobjective optimization ARTIFICIAL INTELLIGENCE multirecombination |
topic |
Ciencias Informáticas Scheduling Evolutionary Computation Flow shop scheduling Optimization multiobjective optimization ARTIFICIAL INTELLIGENCE multirecombination |
dc.description.none.fl_txt_mv |
The Flow Shop Scheduling Problem have been tackled using different techniques which goes from mathematical techniques like Branch and Bound to metaheuristics like evolutionary algorithms (EAs). Although in the real world this problem will be found more frequently with more than one objective, most work been done is based on a single objective. Evolutionary algorithms are very promising in this area because the outcome of a multiobjective problem is a set of optimal solutions (the Pareto Front) which EAs can provide in a single run. Yet another advantage of EA’s over other techniques is that they are less liable to the shape or continuity of the Pareto Front. In this work, we show three implementations of multiobjective Evolutionary Algorithms. The first one uses Single Crossover Per Couple (SCPC), while the other two use Multiple Crossover on Multiple Parents (MCMP), continuing with previous works[7, 8]. These two methods show an enhancement on the performance of the first method. Details of implementation and results are discussed. Eje: Sistemas inteligentes Red de Universidades con Carreras en Informática (RedUNCI) |
description |
The Flow Shop Scheduling Problem have been tackled using different techniques which goes from mathematical techniques like Branch and Bound to metaheuristics like evolutionary algorithms (EAs). Although in the real world this problem will be found more frequently with more than one objective, most work been done is based on a single objective. Evolutionary algorithms are very promising in this area because the outcome of a multiobjective problem is a set of optimal solutions (the Pareto Front) which EAs can provide in a single run. Yet another advantage of EA’s over other techniques is that they are less liable to the shape or continuity of the Pareto Front. In this work, we show three implementations of multiobjective Evolutionary Algorithms. The first one uses Single Crossover Per Couple (SCPC), while the other two use Multiple Crossover on Multiple Parents (MCMP), continuing with previous works[7, 8]. These two methods show an enhancement on the performance of the first method. Details of implementation and results are discussed. |
publishDate |
2002 |
dc.date.none.fl_str_mv |
2002-10 |
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 |
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http://sedici.unlp.edu.ar/handle/10915/23124 |
url |
http://sedici.unlp.edu.ar/handle/10915/23124 |
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spa |
language |
spa |
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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