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Conference Paper: A genetic algorithm for vehicle routing problems with stochastic demand and soft time windows

TitleA genetic algorithm for vehicle routing problems with stochastic demand and soft time windows
Authors
Mak, KLGuo, ZG
Issue Date2004
PublisherIEEE.
Citation
2004 IEEE Systems and Information Engineering Design Symposium, Charlottesville, VA, 16 April 2004. In Proceedings of the 2004 IEEE Systems and Information Engineering Design Symposium, 2004, p. 183-190 How to Cite?
DOI: http://dx.doi.org/10.1109/SIEDS.2004.239880
AbstractThis paper studies the stochastic vehicle routing problem with soft time windows (SVRPSTW). Vehicles with limited capacity are routed from the central depot to a set of geographically dispersed customers with unknown demands, predefined presence probability and time windows. The late arrival at the customer is allowed by adding a penalty to the objective value. A mathematical model is developed to describe the behavior of this kind of delivery system. A novel age based genetic scheduling algorithm is proposed as an optimization tool to solve this intractable vehicle routing problem in order to minimize the total cost. The effectiveness of the proposed scheduling algorithm is illustrated by using a set of randomly generated numerical examples. The results indicate that the proposed genetic approach is a simple but effective means for solving these problems.
Persistent Identifierhttp://hdl.handle.net/10722/46594
References
References in Scopus

 

DC FieldValueLanguage
dc.contributor.authorMak, KLen_HK
dc.contributor.authorGuo, ZGen_HK
dc.date.accessioned2007-10-30T06:53:40Z-
dc.date.available2007-10-30T06:53:40Z-
dc.date.issued2004en_HK
dc.identifier.citation2004 IEEE Systems and Information Engineering Design Symposium, Charlottesville, VA, 16 April 2004. In Proceedings of the 2004 IEEE Systems and Information Engineering Design Symposium, 2004, p. 183-190en_HK
dc.identifier.urihttp://hdl.handle.net/10722/46594-
dc.description.abstractThis paper studies the stochastic vehicle routing problem with soft time windows (SVRPSTW). Vehicles with limited capacity are routed from the central depot to a set of geographically dispersed customers with unknown demands, predefined presence probability and time windows. The late arrival at the customer is allowed by adding a penalty to the objective value. A mathematical model is developed to describe the behavior of this kind of delivery system. A novel age based genetic scheduling algorithm is proposed as an optimization tool to solve this intractable vehicle routing problem in order to minimize the total cost. The effectiveness of the proposed scheduling algorithm is illustrated by using a set of randomly generated numerical examples. The results indicate that the proposed genetic approach is a simple but effective means for solving these problems.en_HK
dc.format.extent354720 bytes-
dc.format.extent2656 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofProceedings of the 2004 IEEE Systems and Information Engineering Design Symposiumen_HK
dc.rights©2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.titleA genetic algorithm for vehicle routing problems with stochastic demand and soft time windowsen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailMak, KL:makkl@hkucc.hku.hken_HK
dc.identifier.authorityMak, KL=rp00154en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/SIEDS.2004.239880-
dc.identifier.scopuseid_2-s2.0-3543091584en_HK
dc.identifier.hkuros86038-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-3543091584&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage183en_HK
dc.identifier.epage190en_HK
dc.identifier.scopusauthoridMak, KL=7102680226en_HK
dc.identifier.scopusauthoridGuo, ZG=7404658503en_HK

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