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This report describes the main ideas of the solution which won the AIcrowdSBB Flatland Challenge 2019-2020, with a score of 99% (meaning that, onaverage, 99% of the agents were routed to their destinations within theallotted time steps). The details of the task can be found on the competition'swebsite. The solution consists of 2 major components: 1) A component which(re-)generates paths over a time-expanded graph for each agent 2) A componentwhich updates the agent paths after a malfunction occurs, in order to try topreserve the same agent ordering of entering each cell as before themalfunction. The goal of this component is twofold: a) to (try to) avoiddeadlocks b) to bring the system back to a consistent state (where each agenthas a feasible path over the time-expanded graph). I am discussing both ofthese components, as well as a series of potentially promising, but unexploredideas, below.

Winning Solution of the AIcrowd SBB Flatland Challenge 2019-2020