Deep Reinforcement Learning for the Capacitated Pickup and Delivery Problem with Time Windows

Abstract

Abstract The vehicle routing problem with pickup and delivery is one of the most important problems in the context of global urban population growth. Although these kinds of small-size problems can be solved using various classical approaches, a fast (or real-time) route optimizer under real-world constraints (such as throughput and time window constraints) for medium- and large-size problems is still a challenge. In this work, we first successfully applied a deep reinforcement learning approach (a modified JAMPR model) to solve the capacitated pickup and delivery problem with time windows (CPDPTW). We obtained a robust model that gives a fast optimal solution for small- to medium-size problems and gives a fast suboptimal solution for large-size (>200) problems.