Fleet Design Optimisation from Historical Data Using Constraint Programming and Large Neighbourhood Search
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 4185-4188.
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.
@inproceedings{ijcai2016p627,
title = {Fleet Design Optimisation from Historical Data Using Constraint Programming and Large Neighbourhood Search},
author = {Kilby, Philip and Urli, Tommaso},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {4185--4188},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/627},
}
TY - CPAPER TI - Fleet Design Optimisation from Historical Data Using Constraint Programming and Large Neighbourhood Search AU - Kilby, Philip AU - Urli, Tommaso T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 4185 EP - 4188 UR - https://ijcai.org/proceedings/2016/627 ER -Copied
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