Fleet Design Optimisation from Historical Data Using Constraint Programming and Large Neighbourhood Search
Abstract
We present an original approach to compute efficient mid-term fleet configurations at the request of a Queensland-based long-haul trucking carrier. Our approach considers one year's worth of demand data, and employs a constraint programming (CP) model and an adaptive large neighbourhood search (LNS) scheme to solve the underlying multi-day multi-commodity split delivery capacitated vehicle routing problem. This paper is an adaptation of the Best Application Paper at CP'15, published in the Constraints journal with the same title. PDF
Cite
Text
Kilby and Urli. "Fleet Design Optimisation from Historical Data Using Constraint Programming and Large Neighbourhood Search." International Joint Conference on Artificial Intelligence, 2016.Markdown
[Kilby and Urli. "Fleet Design Optimisation from Historical Data Using Constraint Programming and Large Neighbourhood Search." International Joint Conference on Artificial Intelligence, 2016.](https://mlanthology.org/ijcai/2016/kilby2016ijcai-fleet/)BibTeX
@inproceedings{kilby2016ijcai-fleet,
title = {{Fleet Design Optimisation from Historical Data Using Constraint Programming and Large Neighbourhood Search}},
author = {Kilby, Philip and Urli, Tommaso},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2016},
pages = {4185-4189},
url = {https://mlanthology.org/ijcai/2016/kilby2016ijcai-fleet/}
}