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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Warisa Wisittipanich | en_US |
dc.contributor.author | Takashi Irohara | en_US |
dc.contributor.author | Piya Hengmeechai | en_US |
dc.date.accessioned | 2020-10-14T08:34:53Z | - |
dc.date.available | 2020-10-14T08:34:53Z | - |
dc.date.issued | 2020-01-01 | en_US |
dc.identifier.issn | 17485045 | en_US |
dc.identifier.issn | 17485037 | en_US |
dc.identifier.other | 2-s2.0-85091042097 | en_US |
dc.identifier.other | 10.1504/IJISE.2020.107778 | en_US |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85091042097&origin=inward | en_US |
dc.identifier.uri | http://cmuir.cmu.ac.th/jspui/handle/6653943832/70587 | - |
dc.description.abstract | Copyright © 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.subject | Engineering | en_US |
dc.title | Particle swarm optimisation for truck scheduling problem in cross docking network | en_US |
dc.type | Journal | en_US |
article.title.sourcetitle | International Journal of Industrial and Systems Engineering | en_US |
article.volume | 35 | en_US |
article.stream.affiliations | Sophia University | en_US |
article.stream.affiliations | Chiang Mai University | en_US |
Appears in Collections: | CMUL: Journal Articles |
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