  The Flatland Challenge, which was first held in 2019 and reported in NeurIPS
2020, is designed to answer the question: How to efficiently manage dense
traffic on complex rail networks? Considering the significance of punctuality
in real-world railway network operation and the fact that fast passenger trains
share the network with slow freight trains, Flatland version 3 introduces
trains with different speeds and scheduling time windows. This paper introduces
the Flatland 3 problem definitions and extends an award-winning MAPF-based
software, which won the NeurIPS 2020 competition, to efficiently solve Flatland
3 problems. The resulting system won the Flatland 3 competition. We designed a
new priority ordering for initial planning, a new neighbourhood selection
strategy for efficient solution quality improvement with Multi-Agent Path
Finding via Large Neighborhood Search(MAPF-LNS), and use MAPF-LNS for partially
replanning the trains influenced by malfunction.
