Scalable Rail Planning and Replanning: Winning the 2020 Flatland Challenge

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, safe interval path planning, parallel computing, simulated annealing, large neighborhood search, and minimum communication policies. It can plan collision-free paths for thousands of trains within a few minutes and deliver deadlock-free actions in real-time during execution.