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Using population-based incremental learning to optimize feasible distribution logistic solutions
Thesis (MScEng (Industrial Engineering))--University of Stellenbosch, 2005.
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| Other Authors: | |
| Format: | Thesis |
| Language: | English |
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Stellenbosch : University of Stellenbosch
2008
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| _version_ | 1867613923795009536 |
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| access_status_str | Open Access |
| author | Lourens, Tobie |
| author2 | Van Wijck, W. |
| author_browse | Lourens, Tobie Van Wijck, W. |
| author_facet | Van Wijck, W. Lourens, Tobie |
| author_sort | Lourens, Tobie |
| collection | Thesis |
| dc_rights_str_mv | University of Stellenbosch |
| description | Thesis (MScEng (Industrial Engineering))--University of Stellenbosch, 2005. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/1601 |
| institution | Stellenbosch University (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:43:51.865Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2008 |
| publishDateRange | 2008 |
| publishDateSort | 2008 |
| publisher | Stellenbosch : University of Stellenbosch |
| publisherStr | Stellenbosch : University of Stellenbosch |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/1601 Using population-based incremental learning to optimize feasible distribution logistic solutions Lourens, Tobie Van Wijck, W. University of Stellenbosch. Faculty of Engineering. Dept. of Industrial Engineering. Dissertations -- Industrial engineering Theses -- Industrial engineering Physical distribution of goods -- Management Thesis (MScEng (Industrial Engineering))--University of Stellenbosch, 2005. 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. 2008-07-17T10:24:15Z 2010-06-01T08:28:23Z 2008-07-17T10:24:15Z 2010-06-01T08:28:23Z 2005-03 Thesis http://hdl.handle.net/10019.1/1601 en University of Stellenbosch application/pdf Stellenbosch : University of Stellenbosch |
| spellingShingle | Dissertations -- Industrial engineering Theses -- Industrial engineering Physical distribution of goods -- Management Lourens, Tobie Using population-based incremental learning to optimize feasible distribution logistic solutions |
| title | Using population-based incremental learning to optimize feasible distribution logistic solutions |
| title_full | Using population-based incremental learning to optimize feasible distribution logistic solutions |
| title_fullStr | Using population-based incremental learning to optimize feasible distribution logistic solutions |
| title_full_unstemmed | Using population-based incremental learning to optimize feasible distribution logistic solutions |
| title_short | Using population-based incremental learning to optimize feasible distribution logistic solutions |
| title_sort | using population based incremental learning to optimize feasible distribution logistic solutions |
| topic | Dissertations -- Industrial engineering Theses -- Industrial engineering Physical distribution of goods -- Management |
| url | http://hdl.handle.net/10019.1/1601 |
| work_keys_str_mv | AT lourenstobie usingpopulationbasedincrementallearningtooptimizefeasibledistributionlogisticsolutions |