Multirecombinated evolutionary algorithms to solve multiobjective job shop scheduling

Multiobjective optimization, also known as vector-valued criteria or multicriteria optimization, have long been used in many application areas where a problem involves multiple objectives, often conflicting, to be met or optimized. Scheduling problems is one of such application areas whose importanc...

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Detalles Bibliográficos
Autores principales: Esquivel, Susana Cecilia, Ferrero, Sergio W., Gallard, Raúl Hector
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2000
Materias:
Ciencias Informáticas
evolutionary computation
job shop scheduling
multiobjective optimization
multirecombination
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/23418
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-23418
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
evolutionary computation
job shop scheduling
multiobjective optimization
multirecombination
spellingShingle Ciencias Informáticas
evolutionary computation
job shop scheduling
multiobjective optimization
multirecombination
Esquivel, Susana Cecilia
Ferrero, Sergio W.
Gallard, Raúl Hector
Multirecombinated evolutionary algorithms to solve multiobjective job shop scheduling
topic_facet Ciencias Informáticas
evolutionary computation
job shop scheduling
multiobjective optimization
multirecombination
description Multiobjective optimization, also known as vector-valued criteria or multicriteria optimization, have long been used in many application areas where a problem involves multiple objectives, often conflicting, to be met or optimized. Scheduling problems is one of such application areas whose importance lays on its economical impact and its complexity. The present paper propases CPS-MCPC, a cooperative population search method with multiple crossovers per couple. The cooperati ve search CPS is implemented with in di viduals of a single population, which are selected for recombination using alternatively each criterion. MCPC a multirecombination approach is used to exploit good features of both selected parents. To test the potentials of the novel method for building the Pareto front regular and non-regular objectives functions were chosen: the makespan and the mean absolute deviation of job completion times from a common due date (an earliness/ tardiness related problem). The set of experiments conducted, used three basic representation schemes and contrasted results of the proposed approach against conventional methods of recombination. Details of implementation and results are discussed.
format Objeto de conferencia
Objeto de conferencia
author Esquivel, Susana Cecilia
Ferrero, Sergio W.
Gallard, Raúl Hector
author_facet Esquivel, Susana Cecilia
Ferrero, Sergio W.
Gallard, Raúl Hector
author_sort Esquivel, Susana Cecilia
title Multirecombinated evolutionary algorithms to solve multiobjective job shop scheduling
title_short Multirecombinated evolutionary algorithms to solve multiobjective job shop scheduling
title_full Multirecombinated evolutionary algorithms to solve multiobjective job shop scheduling
title_fullStr Multirecombinated evolutionary algorithms to solve multiobjective job shop scheduling
title_full_unstemmed Multirecombinated evolutionary algorithms to solve multiobjective job shop scheduling
title_sort multirecombinated evolutionary algorithms to solve multiobjective job shop scheduling
publishDate 2000
url http://sedici.unlp.edu.ar/handle/10915/23418
work_keys_str_mv AT esquivelsusanacecilia multirecombinatedevolutionaryalgorithmstosolvemultiobjectivejobshopscheduling
AT ferrerosergiow multirecombinatedevolutionaryalgorithmstosolvemultiobjectivejobshopscheduling
AT gallardraulhector multirecombinatedevolutionaryalgorithmstosolvemultiobjectivejobshopscheduling
bdutipo_str Repositorios
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