Imran, Arif and Salhi, Said and Wassan, Niaz A. (2009) A variable neighborhood-based heuristic for the heterogeneous fleet vehicle routing problem. European Journal of Operational Research, (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)
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|The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)|
The heterogeneous fleet vehicle routing problem is investigated using some adaptations of the variable neighborhood search (VNS). The initial solution is obtained by Dijkstra's algorithm based on a cost network constructed by the sweep algorithm and the 2-opt. Our VNS algorithm uses several neighborhoods which are adapted for this problem. In addition, a number of local search methods together with a diversification procedure are used. Two VNS variants, which differ in the order the diversification and Dijkstra's algorithm are used, are implemented. Both variants appear to be competitive and produce new best results when tested on the data sets from the literature. We also constructed larger data sets for which benchmarking results are provided for future comparison.
|Uncontrolled keywords:||Meta-heuristic Routing Heterogeneous fleet Variable neighborhood|
|Subjects:||H Social Sciences > H Social Sciences (General)
Q Science > Operations Research - Theory
|Divisions:||Faculties > Social Sciences > Kent Business School > Management Science|
|Depositing User:||Said Salhi|
|Date Deposited:||08 Dec 2009 14:01|
|Last Modified:||02 May 2014 12:04|
|Resource URI:||https://kar.kent.ac.uk/id/eprint/23453 (The current URI for this page, for reference purposes)|
Available versions of this item
A variable neighbourhood-based heuristic for the heterogeneous fleet vehicle routing problem. (deposited 02 Oct 2009 06:37)
- A variable neighborhood-based heuristic for the heterogeneous fleet vehicle routing problem. (deposited 08 Dec 2009 14:01) [Currently Displayed]