A stochastic fleet composition problem
| dc.contributor.author | Loxton, Ryan | |
| dc.contributor.author | Lin, Qun | |
| dc.contributor.author | Teo, Kok Lay | |
| dc.date.accessioned | 2017-01-30T13:27:19Z | |
| dc.date.available | 2017-01-30T13:27:19Z | |
| dc.date.created | 2012-07-04T20:00:21Z | |
| dc.date.issued | 2012 | |
| dc.identifier.citation | Loxton, Ryan and Lin, Qun and Teo, Kok Lay. 2012. A stochastic fleet composition problem. Computers and Operations Research. 39 (12): pp. 3177-3184. | |
| dc.identifier.uri | http://hdl.handle.net/20.500.11937/31771 | |
| dc.identifier.doi | 10.1016/j.cor.2012.04.004 | |
| dc.description.abstract |
In this paper, we consider the problem of forming a new vehicle fleet, consisting of multiple vehicle types, to cater for uncertain future requirements. The problem is to choose the number of vehicles of each type to purchase so that the total expected cost of operating the fleet is minimized. The total expected cost includes fixed and variable costs associated with the fleet, as well as hiring costs that are incurred whenever vehicle requirements exceed fleet capacity. We develop a novel algorithm, which combines dynamic programming and the golden section method, for determining the optimal fleet composition. Numerical results show that this algorithm is highly effective, and takes just seconds to solve large-scale problems involving hundreds of different vehicle types. | |
| dc.publisher | ELSEVIER | |
| dc.subject | Convex optimization | |
| dc.subject | Golden section method | |
| dc.subject | Fleet composition | |
| dc.subject | Dynamic programming | |
| dc.title | A stochastic fleet composition problem | |
| dc.type | Journal Article | |
| dcterms.source.volume | 39 | |
| dcterms.source.number | 12 | |
| dcterms.source.startPage | 3177 | |
| dcterms.source.endPage | 3184 | |
| dcterms.source.issn | 0305-0548 | |
| dcterms.source.title | Computers and Operations Research | |
| curtin.note |
NOTICE: This is the author’s version of a work that was accepted for publication in Computers and Operations Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computers and Operations Research, Vol. 39, Issue 12 (2012). doi: 10.1016/j.cor.2012.04.004 | |
| curtin.department | ||
| curtin.accessStatus | Open access |