Solving the single-track train scheduling problem via Deep Reinforcement Learning

A rail company organizes its fleet to accommodate expected demands, maximizing revenue and coverage, so that the service is provided to customers as far as possible. From a practical point of view, companies have to make decisions for two different time horizons: offline and online. Offline decisions deal with the problem of routing trains in advance, so that the basic path for each train is decided and in normal conditions, these are the one that will be followed. Decisions in this sense are made sporadically in a year, typically once every three to six months. The planned routes and schedules are usually hand-engineered according to regulation, safety measures, and demand requirements. As said, planned routes are the ones preferred in normal conditions, but this rarely happens since disruptions occur daily in the network. A broken train, a not working switch, delays in the preparation of the train, and many more real-life problems may affect the overall network. Sometimes the delay introduced is small and the planned schedule can still be used, but on other occasions, online rerouting and rescheduling have to be applied. In literature, this online decision making is called the Train Dispatching problem (TD), a real-time variant of the Train Timetabling problem (known to be NPhard [3]).