Deep Reinforcement Learning for Electric Vehicle Routing Problem with Time Windows

Abstract

The past decade has seen a rapid penetration of electric vehicles (EV) in the market, more and more logistics and transportation companies start to deploy 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 research, 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 technique 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 any existing approaches.

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• Reinforcement Learning for Ridesharing: An Extended Survey
• Learning for routing: A guided review of recent developments and future directions
• Deep Reinforcement Learning for Demand Driven Services in Logistics and Transportation Systems: A Survey
• Multi-Agent Path Planning Using Deep Reinforcement Learning