Authors: Evertjan Peer, Vlado Menkovski, Yingqian Zhang, Wan-Jui Lee
Abstract The Train Unit Shunting Problem (TUSP) is a difficult sequential decision making problem faced by Dutch Railways (NS). Current heuristic solutions under study at NS fall short in accounting for uncertainty during plan execution and do not efficiently support replanning. Furthermore, the resulting plans lack consistency. We approach the TUSP by formulating it as a Markov Decision Process and develop an image-like state space representation that allows us to develop a Deep Reinforcement Learning (DRL) solution. The Deep Q-Network efficiently reduces the state space and develops an on-line strategy for the TUSP capable of dealing with uncertainty and delivering significantly more consistent solutions compared to approaches currently being developed by NS.