Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/70587
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dc.contributor.authorWarisa Wisittipanichen_US
dc.contributor.authorTakashi Iroharaen_US
dc.contributor.authorPiya Hengmeechaien_US
dc.date.accessioned2020-10-14T08:34:53Z-
dc.date.available2020-10-14T08:34:53Z-
dc.date.issued2020-01-01en_US
dc.identifier.issn17485045en_US
dc.identifier.issn17485037en_US
dc.identifier.other2-s2.0-85091042097en_US
dc.identifier.other10.1504/IJISE.2020.107778en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85091042097&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/70587-
dc.description.abstractCopyright © 2020 Inderscience Enterprises Ltd. In cross docking network, multiple products from multiple origins are transferred by trucks through one or more cross docks. One critical concern is the decision on how to synchronise product transshipment through multiple cross docks to achieve timely shipment. This paper presents a mathematical model of truck scheduling problem in cross docking network in order to minimise makespan. Since the problem is NP-hard, a solution method is developed based on particle swarm optimisation (PSO) with two solution representations: randomised truck solution representation (Ra-SR) and prioritised truck solution representation (Pr-SR). The results show that the PSO-based approach performs well in solving the problem. Both solution representations are proven effective when comparing the solution quality and computational time with optimal results obtained from LINGO. However, the Pr-SR yields superior results to the Ra-SR in terms of solution quality and convergence behaviour for most instances especially in the case of large-size problems.en_US
dc.subjectEngineeringen_US
dc.titleParticle swarm optimisation for truck scheduling problem in cross docking networken_US
dc.typeJournalen_US
article.title.sourcetitleInternational Journal of Industrial and Systems Engineeringen_US
article.volume35en_US
article.stream.affiliationsSophia Universityen_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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