Single Agent Formulation for Reinforcement Learning Based Routing of Urban Last Mile Logistics with Platooning Vehicles

Last mile logistics are in the midst of a deep transformation thanks to the advent of autonomous vehicles with platooning capabilities that can take the place of typical delivery methods. Platooning brings to the vehicle routing problems new constraints and multiple objectives that are addressed in this paper with a Reinforcement Learning approach. In opposition to traditional metaheuristic optimization algorithms, Reinforcement Learning provides flexibility in the face of changing environment, shifting the challenge to the way in which the problem is formulated. While there have been successful attempts to implement RL solutions to vehicle routing problems, including some sort of optional platooning, our main contribution is funded in the application to this platooning vehicle routing problems for last mile delivery, considering all their particularities and proposing a formulation framework for this kind of problems.