Binhui Chen
A variable neighbourhood search algorithm with compound neighbourhoods for VRPTW
Chen, Binhui; Qu, Rong; Bai, Ruibin; Ishibuchi, Hisao
Abstract
The Vehicle Routing Problem with Time Windows (VRPTW) consists of constructing least cost routes from a depot to a set of geographically scattered service points and back to the depot, satisfying service time interval and capacity constraints. A Variable Neighbourhood Search algorithm with Compound Neighbourhoods is proposed to solve VRPTW in this paper. A number of independent neighbourhood operators are composed into compound neighbourhood operators in a new way, to explore wider search area concerning two objectives (to minimize the number of vehicles and the total travel distance) simultaneously. Promising results are obtained on benchmark datasets
Citation
Chen, B., Qu, R., Bai, R., & Ishibuchi, H. (2016). A variable neighbourhood search algorithm with compound neighbourhoods for VRPTW.
Conference Name | The 2016 International Conference on Operations Research and Enterprise Systems |
---|---|
End Date | Feb 25, 2016 |
Acceptance Date | Dec 1, 2015 |
Publication Date | Feb 25, 2016 |
Deposit Date | Jun 10, 2016 |
Publicly Available Date | Jun 10, 2016 |
Peer Reviewed | Peer Reviewed |
Public URL | https://nottingham-repository.worktribe.com/output/775168 |
Publisher URL | http://www.scitepress.org/DigitalLibrary/PublicationsDetail.aspx?ID=SIa0Nd3aHTI=&t=1 |
Additional Information | Published in: Proceedings of 5th the International Conference on Operations Research and Enterprise Systems, pages 25-35.ISBN 9789897581717 DOI: 10.5220/0005661800250035 |
Files
ICORES16vns.pdf
(743 Kb)
PDF
You might also like
Models of Representation in Computational Intelligence [Guest Editorial]
(2023)
Journal Article
Automated algorithm design using proximal policy optimisation with identified features
(2022)
Journal Article
An Efficient Federated Distillation Learning System for Multitask Time Series Classification
(2022)
Journal Article
A Collaborative Learning Tracking Network for Remote Sensing Videos
(2022)
Journal Article