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Genetic algorithms for the SearchCol++ framework : application to drivers’ rostering

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Resumo:This paper presents a new genetic algorithm included in the SearchCol++ framework. The new genetic algorithm includes an elitism strategy and a local search procedure to improve the quality of solutions and performance. The new algorithm is tested in a Bus Driver Rostering Problem decomposition model included in the framework in order to build valid rosters combining subproblems’ solutions, obtained previously by using column generation. Each subproblem solution is a valid work-schedule for the driver corresponding to the subproblem. Computational tests show relevant improvement in the effectiveness and efficiency of the new algorithm to build valid rosters to the BDRP.
Autores principais:Barbosa, Vítor
Outros Autores:Respício, Ana; Alvelos, Filipe Pereira e
Assunto:Genetic algorithm Hybrid optimization methods Column generation Rostering
Ano:2013
País:Portugal
Tipo de documento:comunicação em conferência
Tipo de acesso:acesso restrito
Instituição associada:Universidade do Minho
Idioma:inglês
Origem:RepositóriUM - Universidade do Minho
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author Barbosa, Vítor
author2 Respício, Ana
Alvelos, Filipe Pereira e
author2_role author
author
author_facet Barbosa, Vítor
Respício, Ana
Alvelos, Filipe Pereira e
author_role author
contributor_name_str_mv RepositóriUM - Universidade do Minho
country_str PT
creators_json_txt [{\"Person.name\":\"Barbosa, Vítor\"},{\"Person.name\":\"Respício, Ana\"},{\"Person.name\":\"Alvelos, Filipe Pereira e\"}]
datacite.contributors.contributor.contributorName.fl_str_mv RepositóriUM - Universidade do Minho
datacite.creators.creator.creatorName.fl_str_mv Barbosa, Vítor
Respício, Ana
Alvelos, Filipe Pereira e
datacite.date.Accepted.fl_str_mv 2013-01-01T00:00:00Z
datacite.date.available.fl_str_mv 2013-12-10T13:56:33Z
datacite.date.embargoed.fl_str_mv 2013-12-10T13:56:33Z
datacite.rights.fl_str_mv http://purl.org/coar/access_right/c_16ec
datacite.subjects.subject.fl_str_mv Genetic algorithm
Hybrid optimization methods
Column generation
Rostering
datacite.titles.title.fl_str_mv Genetic algorithms for the SearchCol++ framework : application to drivers’ rostering
dc.contributor.none.fl_str_mv RepositóriUM - Universidade do Minho
dc.creator.none.fl_str_mv Barbosa, Vítor
Respício, Ana
Alvelos, Filipe Pereira e
dc.date.Accepted.fl_str_mv 2013-01-01T00:00:00Z
dc.date.available.fl_str_mv 2013-12-10T13:56:33Z
dc.date.embargoed.fl_str_mv 2013-12-10T13:56:33Z
dc.format.none.fl_str_mv application/pdf
dc.identifier.none.fl_str_mv https://hdl.handle.net/1822/26855
dc.language.none.fl_str_mv eng
dc.publisher.none.fl_str_mv UA - Instituto de Telecomunicações
dc.rights.none.fl_str_mv http://purl.org/coar/access_right/c_16ec
dc.subject.none.fl_str_mv Genetic algorithm
Hybrid optimization methods
Column generation
Rostering
dc.title.fl_str_mv Genetic algorithms for the SearchCol++ framework : application to drivers’ rostering
dc.type.none.fl_str_mv http://purl.org/coar/resource_type/c_5794
description This paper presents a new genetic algorithm included in the SearchCol++ framework. The new genetic algorithm includes an elitism strategy and a local search procedure to improve the quality of solutions and performance. The new algorithm is tested in a Bus Driver Rostering Problem decomposition model included in the framework in order to build valid rosters combining subproblems’ solutions, obtained previously by using column generation. Each subproblem solution is a valid work-schedule for the driver corresponding to the subproblem. Computational tests show relevant improvement in the effectiveness and efficiency of the new algorithm to build valid rosters to the BDRP.
dirty 0
eu_rights_str_mv restrictedAccess
format conferencePaper
fulltext.url.fl_str_mv https://repositorium.uminho.pt/bitstreams/5ef77915-f2f8-4f18-afdf-6f13b02662e4/download
id rum_8564d39b7c68ed6640d39ceb3c1257e3
identifier.url.fl_str_mv https://hdl.handle.net/1822/26855
instacron_str repositorium
institution Universidade do Minho
instname_str Universidade do Minho
language eng
network_acronym_str rum
network_name_str RepositóriUM - Universidade do Minho
oai_identifier_str oai:repositorium.uminho.pt:1822/26855
organization_str_mv urn:organizationAcronym:repositorium
person_str_mv Barbosa, Vítor
Respício, Ana
Alvelos, Filipe Pereira e
publishDate 2013
publisher.none.fl_str_mv UA - Instituto de Telecomunicações
reponame_str RepositóriUM - Universidade do Minho
repository_id_str urn:repositoryAcronym:rum
service_str_mv urn:repositoryAcronym:rum
spelling engUA - Instituto de TelecomunicaçõesporThis paper presents a new genetic algorithm included in the SearchCol++ framework. The new genetic algorithm includes an elitism strategy and a local search procedure to improve the quality of solutions and performance. The new algorithm is tested in a Bus Driver Rostering Problem decomposition model included in the framework in order to build valid rosters combining subproblems’ solutions, obtained previously by using column generation. Each subproblem solution is a valid work-schedule for the driver corresponding to the subproblem. Computational tests show relevant improvement in the effectiveness and efficiency of the new algorithm to build valid rosters to the BDRP.application/pdfporGenetic algorithms for the SearchCol++ framework : application to drivers’ rosteringBarbosa, VítorRespício, AnaAlvelos, Filipe Pereira eHostingInstitutionOrganizationalRepositóriUM - Universidade do Minhoe-mailmailto:repositorium@usdb.uminho.ptrepositorium@usdb.uminho.ptCITATIONV. Barbosa, A. Respício, F. Alvelos, Genetic Algorithms for the SearchCol++ framework: application to drivers’ rostering, actas do XVI Congresso da Associação Portuguesa de Investigação Operacional, Bragança, 3 a 5 de Junho de 2013, 10 pp.2013-12-10T13:56:33Z20132013-01-01T00:00:00ZHandlehttps://hdl.handle.net/1822/26855http://purl.org/coar/access_right/c_16ecrestricted accessGenetic algorithmHybrid optimization methodsColumn generationRostering466034 bytesother research producthttp://purl.org/coar/resource_type/c_5794conference paperhttp://purl.org/coar/access_right/c_16ecapplication/pdffulltexthttps://repositorium.uminho.pt/bitstreams/5ef77915-f2f8-4f18-afdf-6f13b02662e4/download
spellingShingle Genetic algorithms for the SearchCol++ framework : application to drivers’ rostering
Barbosa, Vítor
Genetic algorithm
Hybrid optimization methods
Column generation
Rostering
status SINGLETON
subject.fl_str_mv Genetic algorithm
Hybrid optimization methods
Column generation
Rostering
title Genetic algorithms for the SearchCol++ framework : application to drivers’ rostering
title_full Genetic algorithms for the SearchCol++ framework : application to drivers’ rostering
title_fullStr Genetic algorithms for the SearchCol++ framework : application to drivers’ rostering
title_full_unstemmed Genetic algorithms for the SearchCol++ framework : application to drivers’ rostering
title_short Genetic algorithms for the SearchCol++ framework : application to drivers’ rostering
title_sort Genetic algorithms for the SearchCol++ framework : application to drivers’ rostering
topic Genetic algorithm
Hybrid optimization methods
Column generation
Rostering
topic_facet Genetic algorithm
Hybrid optimization methods
Column generation
Rostering
url https://hdl.handle.net/1822/26855
visible 1