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Conference Paper: An artificial bee colony algorithm for public bike repositioning problem

TitleAn artificial bee colony algorithm for public bike repositioning problem
Authors
Issue Date2015
PublisherThe National Academies of Sciences, Engineering, and Medicine.
Citation
The 37th Australasian Transport Research Forum (ATRF 2015), Sydney, Australia, 30 September-2 October 2015, p. 1-12 How to Cite?
AbstractPublic bike repositioning is crucial in public bike sharing systems due to the imbalanced distribution of public bikes. This paper models the public bike repositioning problem (PBRP) involving two non-linear objectives, which are to minimize total service duration and the duration of the longest vehicle route. It includes practical constraints such as the tolerance of demand dissatisfaction and the limitation of duration on the longest route. These objective functions and constraints make the PBRP become NP-hard, so here introduces an artificial bee colony (ABC) algorithm to solve this PBRP. Three neighbourhood operators are introduced to improve the solution search. A modified ABC is proposed to further improve the solution quality. The performance of the modified heuristic was evaluated with the network of Vélib', and compared with the original heuristic and the Genetic Algorithm. These results may therefore prove that the modified heuristic can be an alternative to solve the PBRP. The numerical studies demonstrated that the two objective functions performed differently in which the increase in fleet size may not improve the objective value. This paper will therefore discuss on the practical implications of the trade-offs and provide suggestions about similar repositioning operations.
DescriptionConference Theme: Informing transport’s future through practical research
Paper Presentation
Persistent Identifierhttp://hdl.handle.net/10722/226533

 

DC FieldValueLanguage
dc.contributor.authorShui, CS-
dc.contributor.authorSzeto, WY-
dc.date.accessioned2016-06-17T07:44:44Z-
dc.date.available2016-06-17T07:44:44Z-
dc.date.issued2015-
dc.identifier.citationThe 37th Australasian Transport Research Forum (ATRF 2015), Sydney, Australia, 30 September-2 October 2015, p. 1-12-
dc.identifier.urihttp://hdl.handle.net/10722/226533-
dc.descriptionConference Theme: Informing transport’s future through practical research-
dc.descriptionPaper Presentation-
dc.description.abstractPublic bike repositioning is crucial in public bike sharing systems due to the imbalanced distribution of public bikes. This paper models the public bike repositioning problem (PBRP) involving two non-linear objectives, which are to minimize total service duration and the duration of the longest vehicle route. It includes practical constraints such as the tolerance of demand dissatisfaction and the limitation of duration on the longest route. These objective functions and constraints make the PBRP become NP-hard, so here introduces an artificial bee colony (ABC) algorithm to solve this PBRP. Three neighbourhood operators are introduced to improve the solution search. A modified ABC is proposed to further improve the solution quality. The performance of the modified heuristic was evaluated with the network of Vélib', and compared with the original heuristic and the Genetic Algorithm. These results may therefore prove that the modified heuristic can be an alternative to solve the PBRP. The numerical studies demonstrated that the two objective functions performed differently in which the increase in fleet size may not improve the objective value. This paper will therefore discuss on the practical implications of the trade-offs and provide suggestions about similar repositioning operations.-
dc.languageeng-
dc.publisherThe National Academies of Sciences, Engineering, and Medicine.-
dc.relation.ispartofAustralasian Transport Research Forum, ATRF 2015-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleAn artificial bee colony algorithm for public bike repositioning problem-
dc.typeConference_Paper-
dc.identifier.emailSzeto, WY: ceszeto@hku.hk-
dc.identifier.authoritySzeto, WY=rp01377-
dc.description.naturepostprint-
dc.identifier.hkuros258167-
dc.identifier.spage1-
dc.identifier.epage12-
dc.publisher.placeUnited States-

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