Contrasting two MCMP alternatives in evolutionary algorithms to solve the job shop scheduling problem

Many researchers have shown interest to solve the job shop scheduling problem (JSSP) applying evolutionary algorithms (EAs). In a previous work we reported an enhanced evolutionary algorithm, which uses a multiplicity feature to solve JSSP. The evolutionary approach was enhanced by means of multiple...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Stark, Natalia, Salto, Carolina, Alfonso, Hugo, Gallard, Raúl Hector
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2001
Materias:
Ciencias Informáticas
Optimization
Scheduling
ARTIFICIAL INTELLIGENCE
scanning crossover
breeding
Multirecombination
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/23413
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-23413
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
Optimization
Scheduling
ARTIFICIAL INTELLIGENCE
scanning crossover
breeding
Multirecombination
spellingShingle Ciencias Informáticas
Optimization
Scheduling
ARTIFICIAL INTELLIGENCE
scanning crossover
breeding
Multirecombination
Stark, Natalia
Salto, Carolina
Alfonso, Hugo
Gallard, Raúl Hector
Contrasting two MCMP alternatives in evolutionary algorithms to solve the job shop scheduling problem
topic_facet Ciencias Informáticas
Optimization
Scheduling
ARTIFICIAL INTELLIGENCE
scanning crossover
breeding
Multirecombination
description Many researchers have shown interest to solve the job shop scheduling problem (JSSP) applying evolutionary algorithms (EAs). In a previous work we reported an enhanced evolutionary algorithm, which uses a multiplicity feature to solve JSSP. The evolutionary approach was enhanced by means of multiple crossovers on multiple parents (MCMP) and the selection of a stud among the intervening parent. Partially mapped crossover (PMX) was used on each multiple crossover operation and job based representation (permutation of jobs) was adopted as a coding technique. The traditional MCMP approach is based on scanning crossover. But the application of this operator to permutations will yield illegal offspring in the sense that some jobs may be missed while some other jobs may be duplicated in the offspring, so some modifications to their mechanism are necessary to guarantee the offspring legality. This paper contrasts both MCMP approaches, discusses implementation details and shows results for a set of job shop scheduling instances of distinct complexity.
format Objeto de conferencia
Objeto de conferencia
author Stark, Natalia
Salto, Carolina
Alfonso, Hugo
Gallard, Raúl Hector
author_facet Stark, Natalia
Salto, Carolina
Alfonso, Hugo
Gallard, Raúl Hector
author_sort Stark, Natalia
title Contrasting two MCMP alternatives in evolutionary algorithms to solve the job shop scheduling problem
title_short Contrasting two MCMP alternatives in evolutionary algorithms to solve the job shop scheduling problem
title_full Contrasting two MCMP alternatives in evolutionary algorithms to solve the job shop scheduling problem
title_fullStr Contrasting two MCMP alternatives in evolutionary algorithms to solve the job shop scheduling problem
title_full_unstemmed Contrasting two MCMP alternatives in evolutionary algorithms to solve the job shop scheduling problem
title_sort contrasting two mcmp alternatives in evolutionary algorithms to solve the job shop scheduling problem
publishDate 2001
url http://sedici.unlp.edu.ar/handle/10915/23413
work_keys_str_mv AT starknatalia contrastingtwomcmpalternativesinevolutionaryalgorithmstosolvethejobshopschedulingproblem
AT saltocarolina contrastingtwomcmpalternativesinevolutionaryalgorithmstosolvethejobshopschedulingproblem
AT alfonsohugo contrastingtwomcmpalternativesinevolutionaryalgorithmstosolvethejobshopschedulingproblem
AT gallardraulhector contrastingtwomcmpalternativesinevolutionaryalgorithmstosolvethejobshopschedulingproblem
bdutipo_str Repositorios
_version_ 1764820465895866368

Ejemplares similares