Multiple crossovers on multiple parents for the multiobjective flow shop problem

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...

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Autores principales: Esquivel, Susana Cecilia, Zuppa, Federico, Gallard, Raúl Hector
Formato: Objeto de conferencia
Lenguaje:Español
Publicado: 2002
Materias:
Ciencias Informáticas
Scheduling
Evolutionary Computation
Flow shop scheduling
Optimization
multiobjective optimization
ARTIFICIAL INTELLIGENCE
multirecombination
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/23124
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-23124
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Español
topic Ciencias Informáticas
Scheduling
Evolutionary Computation
Flow shop scheduling
Optimization
multiobjective optimization
ARTIFICIAL INTELLIGENCE
multirecombination
spellingShingle Ciencias Informáticas
Scheduling
Evolutionary Computation
Flow shop scheduling
Optimization
multiobjective optimization
ARTIFICIAL INTELLIGENCE
multirecombination
Esquivel, Susana Cecilia
Zuppa, Federico
Gallard, Raúl Hector
Multiple crossovers on multiple parents for the multiobjective flow shop problem
topic_facet Ciencias Informáticas
Scheduling
Evolutionary Computation
Flow shop scheduling
Optimization
multiobjective optimization
ARTIFICIAL INTELLIGENCE
multirecombination
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.
format Objeto de conferencia
Objeto de conferencia
author Esquivel, Susana Cecilia
Zuppa, Federico
Gallard, Raúl Hector
author_facet Esquivel, Susana Cecilia
Zuppa, Federico
Gallard, Raúl Hector
author_sort Esquivel, Susana Cecilia
title Multiple crossovers on multiple parents for the multiobjective flow shop problem
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
publishDate 2002
url http://sedici.unlp.edu.ar/handle/10915/23124
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AT zuppafederico multiplecrossoversonmultipleparentsforthemultiobjectiveflowshopproblem
AT gallardraulhector multiplecrossoversonmultipleparentsforthemultiobjectiveflowshopproblem
bdutipo_str Repositorios
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