Deep Reinforcement Learning for the Electric Vehicle Routing Problem With Time Windows

The past decade has seen a rapid penetration of electric vehicles (EVs) as more and more logistics and transportation companies start to deploy electric vehicles (EVs) for service provision. In order to model the operations of a commercial EV fleet, we utilize the EV routing problem with time windows (EVRPTW). In this paper, we propose an end-to-end deep reinforcement learning framework to solve the EVRPTW. In particular, we develop an attention model incorporating the pointer network and a graph embedding layer to parameterize a stochastic policy for solving the EVRPTW. The model is then trained using policy gradient with rollout baseline. Our numerical studies show that the proposed model is able to efficiently solve EVRPTW instances of large sizes that are not solvable with current existing approaches.

Automated summary

In the past decade, there has been a rapid penetration of electric vehicles in the logistics and transportation industry. This paper proposes an end-to-end deep reinforcement learning framework utilizing an attention model and graph embedding layer to efficiently solve the electric vehicle routing problem with time windows. The developed model shows promise in solving large instances that current existing approaches are unable to handle.