Bi-objective Evolutionary Heuristics for Bus Drivers

Detalhes bibliográficos
Autor(a) principal: Moz, Margarida
Data de Publicação: 2007
Outros Autores: Respício, Ana, Pato, Margarida Vaz
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.5/1423
Resumo: The Bus Driver Rostering Problem refers to the assignment of drivers to the daily schedules of the company's buses, during a planning period of a given duration. The drivers' schedules must comply with legal and institutional rules, namely the Labour Law, labour agreements and the company's specific regulations. This paper presents a bi-objective model for the problem and two evolutionary heuristics differing as to the strategies adopted to approach the Pareto frontier. The first one, the utopian strategy, extends elitism to include an unfeasible solution in the population, and the second one is an adapted version of the well known SPEA2 (Strength Pareto Evolutionary Algorithm). The heuristics' empirical performance is studied with computational tests on a set of instances generated from vehicle and crew schedules. This research shows that both methodologies are adequate to tackle the instances of the Bus Driver Rostering Problem. In fact, in short computing times, they provide the planning department, with several feasible solutions, rosters that are very difficult to obtain manually and, in addition, identify among them the efficient solutions of the bi-objective model.
id RCAP_a337f21ce0e099dffc5f6ae2530d5c0f
oai_identifier_str oai:www.repository.utl.pt:10400.5/1423
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling Bi-objective Evolutionary Heuristics for Bus Driversbus drivers rosteringbi-objective problemsgenetic algorithmsThe Bus Driver Rostering Problem refers to the assignment of drivers to the daily schedules of the company's buses, during a planning period of a given duration. The drivers' schedules must comply with legal and institutional rules, namely the Labour Law, labour agreements and the company's specific regulations. This paper presents a bi-objective model for the problem and two evolutionary heuristics differing as to the strategies adopted to approach the Pareto frontier. The first one, the utopian strategy, extends elitism to include an unfeasible solution in the population, and the second one is an adapted version of the well known SPEA2 (Strength Pareto Evolutionary Algorithm). The heuristics' empirical performance is studied with computational tests on a set of instances generated from vehicle and crew schedules. This research shows that both methodologies are adequate to tackle the instances of the Bus Driver Rostering Problem. In fact, in short computing times, they provide the planning department, with several feasible solutions, rosters that are very difficult to obtain manually and, in addition, identify among them the efficient solutions of the bi-objective model.Centro de Investigação Operacional - Universidade de LisboaRepositório da Universidade de LisboaMoz, MargaridaRespício, AnaPato, Margarida Vaz2009-11-05T12:41:19Z20072007-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.5/1423engMoz, Margarida, Ana Respício e Margarida Vaz Pato. 2007. "Bi-objective Evolutionary Heuristics for Bus Drivers". Universidade de Lisboa – Centro de Investigação Operacional CIO - Working paper nº 1/2007info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-03-06T14:32:41Zoai:www.repository.utl.pt:10400.5/1423Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T16:49:33.759892Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Bi-objective Evolutionary Heuristics for Bus Drivers
title Bi-objective Evolutionary Heuristics for Bus Drivers
spellingShingle Bi-objective Evolutionary Heuristics for Bus Drivers
Moz, Margarida
bus drivers rostering
bi-objective problems
genetic algorithms
title_short Bi-objective Evolutionary Heuristics for Bus Drivers
title_full Bi-objective Evolutionary Heuristics for Bus Drivers
title_fullStr Bi-objective Evolutionary Heuristics for Bus Drivers
title_full_unstemmed Bi-objective Evolutionary Heuristics for Bus Drivers
title_sort Bi-objective Evolutionary Heuristics for Bus Drivers
author Moz, Margarida
author_facet Moz, Margarida
Respício, Ana
Pato, Margarida Vaz
author_role author
author2 Respício, Ana
Pato, Margarida Vaz
author2_role author
author
dc.contributor.none.fl_str_mv Repositório da Universidade de Lisboa
dc.contributor.author.fl_str_mv Moz, Margarida
Respício, Ana
Pato, Margarida Vaz
dc.subject.por.fl_str_mv bus drivers rostering
bi-objective problems
genetic algorithms
topic bus drivers rostering
bi-objective problems
genetic algorithms
description The Bus Driver Rostering Problem refers to the assignment of drivers to the daily schedules of the company's buses, during a planning period of a given duration. The drivers' schedules must comply with legal and institutional rules, namely the Labour Law, labour agreements and the company's specific regulations. This paper presents a bi-objective model for the problem and two evolutionary heuristics differing as to the strategies adopted to approach the Pareto frontier. The first one, the utopian strategy, extends elitism to include an unfeasible solution in the population, and the second one is an adapted version of the well known SPEA2 (Strength Pareto Evolutionary Algorithm). The heuristics' empirical performance is studied with computational tests on a set of instances generated from vehicle and crew schedules. This research shows that both methodologies are adequate to tackle the instances of the Bus Driver Rostering Problem. In fact, in short computing times, they provide the planning department, with several feasible solutions, rosters that are very difficult to obtain manually and, in addition, identify among them the efficient solutions of the bi-objective model.
publishDate 2007
dc.date.none.fl_str_mv 2007
2007-01-01T00:00:00Z
2009-11-05T12:41:19Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.5/1423
url http://hdl.handle.net/10400.5/1423
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Moz, Margarida, Ana Respício e Margarida Vaz Pato. 2007. "Bi-objective Evolutionary Heuristics for Bus Drivers". Universidade de Lisboa – Centro de Investigação Operacional CIO - Working paper nº 1/2007
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Centro de Investigação Operacional - Universidade de Lisboa
publisher.none.fl_str_mv Centro de Investigação Operacional - Universidade de Lisboa
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
_version_ 1799130969619300352