Deep Reinforcement Learning solution for pickup and delivery routing problems with time window and capacity constraints
Researchers from Moscow State University developed a modified JAMPR model using deep reinforcement learning to solve complex pickup and delivery routing problems. The model manages capacity and time window constraints through soft costs and masking. While requiring intensive upfront GPU training, it provides greater reliability and speed for large-scale logistics than traditional solvers.