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

SUNScholar Research Repository

Show simple item record

dc.contributor.advisor Van Wijck, W.
dc.contributor.author Lourens, Tobie en_ZA
dc.contributor.other University of Stellenbosch. Faculty of Engineering. Dept. of Industrial Engineering.
dc.date.accessioned 2008-07-17T10:24:15Z en_ZA
dc.date.accessioned 2010-06-01T08:28:23Z
dc.date.available 2008-07-17T10:24:15Z en_ZA
dc.date.available 2010-06-01T08:28:23Z
dc.date.issued 2005-03 en_ZA
dc.identifier.uri http://hdl.handle.net/10019.1/1601
dc.description Thesis (MScEng (Industrial Engineering))--University of Stellenbosch, 2005.
dc.description.abstract This 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.language.iso en en_ZA
dc.publisher Stellenbosch : University of Stellenbosch
dc.subject Dissertations -- Industrial engineering en
dc.subject Theses -- Industrial engineering en
dc.subject Physical distribution of goods -- Management en
dc.title Using population-based incremental learning to optimize feasible distribution logistic solutions en_ZA
dc.type Thesis en_ZA
dc.rights.holder University of Stellenbosch


Files in this item

This item appears in the following Collection(s)

Show simple item record