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Title: Scalable Rail Planning and Replanning: Winning the 2020 Flatland Challenge
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.  more » « less
Award ID(s):
1837779
NSF-PAR ID:
10296532
Author(s) / Creator(s):
; ; ; ; ; ; ;
Date Published:
Journal Name:
Proceedings of the International Conference on Automated Planning and Scheduling
ISSN:
2334-0843
Page Range / eLocation ID:
477-485
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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