Deep Reinforcement Learning for Stochastic Dynamic Vehicle Routing Problem

In this study, we propose solving the Stochastic Dynamic Vehicle Routing Problem with Deep Reinforcement Learning. We propose a fully attention-based model consisting of a dynamic encoder and decoder to learn the routing policy of a single vehicle, where the customer orders appear over time. To train the model, we utilize a reinforcement learning setting, in which a reward signal guides an agent to select nodes to be visited. The computational analyses demonstrate the suggested strategy's superiority over the chosen benchmark algorithms.