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dc.contributor.authorChen, Mu-Chenen_US
dc.contributor.authorHsiao, Yu-Hsiangen_US
dc.contributor.authorReddy, Reddivari Himadeepen_US
dc.contributor.authorTiwari, Manoj Kumaren_US
dc.date.accessioned2017-04-21T06:56:38Z-
dc.date.available2017-04-21T06:56:38Z-
dc.date.issued2016-07en_US
dc.identifier.issn1366-5545en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.tre.2016.04.003en_US
dc.identifier.urihttp://hdl.handle.net/11536/133908-
dc.description.abstractVehicle Routing Problems (VRPs) in distribution centers with cross-docking operations are more complex than the traditional ones. This paper attempts to address the VRP of distribution centers with multiple cross-docks for processing multiple products. In this paper, the mathematical model intends to minimize the total cost of operations subjected to a set of constraints. Due to high complexity of model, it is solved by using a variant of Particle Swarm Optimization (PSO) with a Self-Learning strategy, namely SLPSO. To validate the effectiveness of SLPSO approach, benchmark problems in the literature and test problems are solved by SLPSO. (C) 2016 Elsevier Ltd. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectMultiple cross-docksen_US
dc.subjectVehicle routing problemen_US
dc.subjectParticle Swarm Optimizationen_US
dc.subjectSelf-learning strategyen_US
dc.subjectGenetic Algorithmsen_US
dc.titleThe Self-Learning Particle Swarm Optimization approach for routing pickup and delivery of multiple products with material handling in multiple cross-docksen_US
dc.identifier.doi10.1016/j.tre.2016.04.003en_US
dc.identifier.journalTRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEWen_US
dc.citation.volume91en_US
dc.citation.spage208en_US
dc.citation.epage226en_US
dc.contributor.department運輸與物流管理系 註:原交通所+運管所zh_TW
dc.contributor.departmentDepartment of Transportation and Logistics Managementen_US
dc.identifier.wosnumberWOS:000378180600012en_US
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