We describe our submission to the Amazon Last Mile Routing Research Challenge. The optimization method we employ utilizes a simple and ecient penalty-based local-search algorithm, rst developed by Helsgaun to extend the LKH traveling salesman problem code to general vehicle-routing models. We further develop his technique to handle combinations of routing constraints that are learned from an analysis of historical data. On a target set of 1,107 training instances, our submitted code achieves a mean score of 0.01989 and a median score of 0.00752. The simplicity of the method may make it suitable for applications where machine learning can discover rules that are expected (or desired) in high-quality solutions.
|Titel||Technical Proceedings of the 2021 Amazon Last Mile Routing Research Challenge|
|Redaktører||Matthias Winkenbach, Steven Parks, Joseph Noszek|
|Status||Udgivet - sep. 2021|
|Begivenhed||Amazon Last-Mile Routing Research Challenge - |
Varighed: 22 feb. 2021 → 30 jul. 2021
|Andet||Amazon Last-Mile Routing Research Challenge|
|Periode||22/02/2021 → 30/07/2021|