Scalable Rail Planning and Replanning: Winning the 2020 Flatland Challenge - Robotics Institute Carnegie Mellon University

Scalable Rail Planning and Replanning: Winning the 2020 Flatland Challenge

Jiaoyang Li, Zhe Chen, Yi Zheng, Shao-Hung Chan, Daniel Harabor, Peter J. Stuckey, Hang Ma, and Sven Koenig
Conference Paper, Proceedings of 31th International Conference on Automated Planning and Scheduling (ICAPS '21), Vol. 31, pp. 477 - 485, May, 2021

Abstract

Multi-Agent Path Finding (MAPF) is the combinatorial problem of finding collision-free paths for multiple agents on a graph. This paper describes MAPF-based software for solving train planning and replanning problems on large-scale rail networks under uncertainty. The software recently won the 2020 Flatland Challenge, a NeurIPS competition trying to determine how to efficiently manage dense traffic on rail networks. The software incorporates many state-of-the-art MAPF or, in general, optimization technologies, such as prioritized planning, large neighborhood search, safe interval path planning, minimum communication policies, parallel computing, and simulated annealing. It can plan collision-free paths for thousands of trains within a few minutes and deliver deadlock-free actions in real-time during execution.

BibTeX

@conference{Li-2021-131395,
author = {Jiaoyang Li and Zhe Chen and Yi Zheng and Shao-Hung Chan and Daniel Harabor and Peter J. Stuckey and Hang Ma and Sven Koenig},
title = {Scalable Rail Planning and Replanning: Winning the 2020 Flatland Challenge},
booktitle = {Proceedings of 31th International Conference on Automated Planning and Scheduling (ICAPS '21)},
year = {2021},
month = {May},
volume = {31},
pages = {477 - 485},
}