Bi-objective Evolutionary Heuristics for Bus Drivers
Autor(a) principal: | |
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Data de Publicação: | 2007 |
Outros Autores: | , |
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. |
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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 |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799130969619300352 |