---
title: "Fleet Design Optimisation from Historical Data Using Constraint Programming and Large Neighbourhood Search"
authors:
  - "Philip Kilby"
  - "Tommaso Urli"
doi: ""
url: "https://ijcai.org/proceedings/2016/627"
pdf: "https://ijcai.org/proceedings/2016/0627.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "4185-4188"
track: ""
keywords:
---

# Fleet Design Optimisation from Historical Data Using Constraint Programming and Large Neighbourhood Search

Philip Kilby, Tommaso Urli

*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.