Proceedings Abstracts of the Twenty-Fifth International Joint Conference on Artificial Intelligence

Fleet Design Optimisation from Historical Data Using Constraint Programming and Large Neighbourhood Search / 4185
Philip Kilby, Tommaso Urli

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.