Adding problem-specific knowledge in evolutionary algorithms to solve W-T scheduling problems

In a production system it is usual to stress minimum tardiness to achieve higher client satisfaction. According to the client relevance, job processing costs and requirements, and various other considerations, a weight is assigned to each job. An important, non-trivial, problem is to minimize weigh...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: San Pedro, María Eugenia de, Pandolfi, Daniel, Villagra, Andrea, Lasso, Marta Graciela, Vilanova, Gabriela, Gallard, Raúl Hector
Formato: Objeto de conferencia
Lenguaje:Español
Publicado: 2002
Materias:
Ciencias Informáticas
Evolutionary Algorithms
Algorithms
Solve W-T Scheduling Problems
Scheduling
ARTIFICIAL INTELLIGENCE
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/23134
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-23134
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
Evolutionary Algorithms
Algorithms
Solve W-T Scheduling Problems
Scheduling
ARTIFICIAL INTELLIGENCE
spellingShingle Ciencias Informáticas
Evolutionary Algorithms
Algorithms
Solve W-T Scheduling Problems
Scheduling
ARTIFICIAL INTELLIGENCE
San Pedro, María Eugenia de
Pandolfi, Daniel
Villagra, Andrea
Lasso, Marta Graciela
Vilanova, Gabriela
Gallard, Raúl Hector
Adding problem-specific knowledge in evolutionary algorithms to solve W-T scheduling problems
topic_facet Ciencias Informáticas
Evolutionary Algorithms
Algorithms
Solve W-T Scheduling Problems
Scheduling
ARTIFICIAL INTELLIGENCE
description In a production system it is usual to stress minimum tardiness to achieve higher client satisfaction. According to the client relevance, job processing costs and requirements, and various other considerations, a weight is assigned to each job. An important, non-trivial, problem is to minimize weighted tardiness. Evolutionary algorithms (EAs) have been proved as efficient tools to solve scheduling problems. Latest improvements in EAs have been developed by means of multirecombination, a method which allows multiple exchange of genetic material between individuals of the mating pool. As EAs are blind search methods this paper proposes to insert problem-specific-knowledge by recombining potential solutions (individuals of the evolving population) with seeds, which are solutions provided by other heuristics specifically intended to solve the scheduling problem under study. In this work we describe two main approaches where seeds are inserted either in the initial population or as a part of every mating pool during evolution. Both methods were contrasted for a set of problem instances extracted from the OR-Library. An outline of the weighted tardiness problem in a single machine environment, details of implementation and results are discussed.
format Objeto de conferencia
Objeto de conferencia
author San Pedro, María Eugenia de
Pandolfi, Daniel
Villagra, Andrea
Lasso, Marta Graciela
Vilanova, Gabriela
Gallard, Raúl Hector
author_facet San Pedro, María Eugenia de
Pandolfi, Daniel
Villagra, Andrea
Lasso, Marta Graciela
Vilanova, Gabriela
Gallard, Raúl Hector
author_sort San Pedro, María Eugenia de
title Adding problem-specific knowledge in evolutionary algorithms to solve W-T scheduling problems
title_short Adding problem-specific knowledge in evolutionary algorithms to solve W-T scheduling problems
title_full Adding problem-specific knowledge in evolutionary algorithms to solve W-T scheduling problems
title_fullStr Adding problem-specific knowledge in evolutionary algorithms to solve W-T scheduling problems
title_full_unstemmed Adding problem-specific knowledge in evolutionary algorithms to solve W-T scheduling problems
title_sort adding problem-specific knowledge in evolutionary algorithms to solve w-t scheduling problems
publishDate 2002
url http://sedici.unlp.edu.ar/handle/10915/23134
work_keys_str_mv AT sanpedromariaeugeniade addingproblemspecificknowledgeinevolutionaryalgorithmstosolvewtschedulingproblems
AT pandolfidaniel addingproblemspecificknowledgeinevolutionaryalgorithmstosolvewtschedulingproblems
AT villagraandrea addingproblemspecificknowledgeinevolutionaryalgorithmstosolvewtschedulingproblems
AT lassomartagraciela addingproblemspecificknowledgeinevolutionaryalgorithmstosolvewtschedulingproblems
AT vilanovagabriela addingproblemspecificknowledgeinevolutionaryalgorithmstosolvewtschedulingproblems
AT gallardraulhector addingproblemspecificknowledgeinevolutionaryalgorithmstosolvewtschedulingproblems
bdutipo_str Repositorios
_version_ 1764820465687199745

Ejemplares similares