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.

翻译：Flatland挑战赛首次于2019年举办，并于NeurIPS 2020会议报道，旨在回答如何高效管理复杂铁路网络上的密集交通这一问题。考虑到准时性在现实铁路网络运营中的重要性，以及快速客运列车与慢速货运列车共享铁路网络的现实情况，Flatland第3版引入了具有不同速度和时间窗约束的列车。本文介绍了Flatland 3的问题定义，并扩展了赢得NeurIPS 2020竞赛的基于MAPF的获奖软件，以高效解决Flatland 3问题。最终系统赢得了Flatland 3竞赛。我们为初始规划设计了新的优先排序规则，为通过大规模邻域搜索的多智能体路径规划（MAPF-LNS）实现高效解质量改进设计了新的邻域选择策略，并利用MAPF-LNS对受故障影响的列车进行部分重规划。