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
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/23124

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network_name_str SEDICI (UNLP)
spelling 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
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status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/23124
url http://sedici.unlp.edu.ar/handle/10915/23124
dc.language.none.fl_str_mv 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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Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
401-410
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