Deep Reinforcement Learning solution for pickup and delivery routing problems with time window and capacity constraints

Researchers at an unnamed institution applied a modified JAMPR deep reinforcement learning model to solve pickup and delivery problems with capacity and time window constraints (CPDPTW), achieving fast optimal solutions for small and medium problems and fast suboptimal solutions for larger problems over 200 nodes. The work, posted on arXiv (2608.14156v1), marks the first successful application of deep reinforcement learning to CPDPTW.

arXiv:2608.14156v1 Announce Type: new Abstract: The task of constructing vehicles optimal routes for pickup and delivery of goods is one of most promising tasks in the context of global urban population growth. Although this kind of problems with small size can be solved by various classical approaches, a fast or realtime route optimizer under the constraints of the real world such as capacity and time windows constraints for medium-large size problems still remains a highly challenging task. In this work we, for the first time, successfully applied a deep Reinforcing Learning approach modified JAMPR model to solve Pickup and Delivery problem with Capacity and Time Window constraints CPDPTW . We obtained a robust model that gives a fast optimal solution for problems of small and medium size, and gives fast suboptimal solution for problems of larger 200 size.