A semi-hierarchical RL approach for railway rescheduling nearly doubles the number of trains reaching destinations in simulations while keeping deadlock rates below 5%.
Using constraint programming and local search methods to solve vehicle routing problems
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Towards Autonomous Railway Operations: A Semi-Hierarchical Deep Reinforcement Learning Approach to the Vehicle Rescheduling Problem
A semi-hierarchical RL approach for railway rescheduling nearly doubles the number of trains reaching destinations in simulations while keeping deadlock rates below 5%.