Using population-based incremental learning to optimize feasible distribution logistic solutions

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dc.contributor.advisorVan Wijck, W.
dc.contributor.authorLourens, Tobieen_ZA
dc.contributor.otherUniversity of Stellenbosch. Faculty of Engineering. Dept. of Industrial Engineering.
dc.date.accessioned2008-07-17T10:24:15Zen_ZA
dc.date.accessioned2010-06-01T08:28:23Z
dc.date.available2008-07-17T10:24:15Zen_ZA
dc.date.available2010-06-01T08:28:23Z
dc.date.issued2005-03en_ZA
dc.descriptionThesis (MScEng (Industrial Engineering))--University of Stellenbosch, 2005.
dc.description.abstractThis thesis introduces an adaptation of the Population-Based Incremental Learning (PBIL) meta-heuristic implemented on a variant of the General Pickup and Delivery Problem. The mapping of the customers in the problem and the vehicle routes on a time grid enables the utilization of the powerful genetic search that the PBIL algorithm provides in liaison with competitive learning. The problem consists of a number of customers who may at any time of the day place an order on another customer for some package. The fleet of vehicles travelling between the customers must then combine powers to pickup and deliver the package as fast as possible without ever leaving their assigned routes. The solution to this problem then, is a set of routes for the fleet that will minimize some percentile of the delivery times between customers. The PBIL meta-heuristic provides the blueprint of the final algorithm, where the final algorithm is actually just a normal PBIL algorithm with some external solution generation and evaluation techniques employed. The final algorithm can easily solve an instance of the problem in polynomial time, given that the resolution of the time grid used is not too small.en_ZA
dc.identifier.urihttp://hdl.handle.net/10019.1/1601
dc.language.isoenen_ZA
dc.publisherStellenbosch : University of Stellenbosch
dc.rights.holderUniversity of Stellenbosch
dc.subjectDissertations -- Industrial engineeringen
dc.subjectTheses -- Industrial engineeringen
dc.subjectPhysical distribution of goods -- Managementen
dc.titleUsing population-based incremental learning to optimize feasible distribution logistic solutionsen_ZA
dc.typeThesisen_ZA
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