A Review About Electric Vehicle Routing Problem with Reinforcement Learning

Abstract

The Electric Vehicle Routing Problem (EVRP) is a variant of the traditional Vehicle Routing Problem (VRP) that deals explicitly with the routing and scheduling of electric vehicles (EVs). It considers EVs' unique constraints and characteristics, such as limited driving range and the need for battery charging. Reinforcement Learning (RL) is a type of machine learning that involves training an agent to make a series of decisions in an environment to maximize a reward. RL has been successfully applied to various problems, including game-playing, robotics, and decision-making under uncertainty. Some key challenges in RL include dealing with large state and action spaces, balancing exploration and exploitation, and dealing with non-stationary environments. RL has emerged as a promising approach for solving the EVRP in recent years. In the context of the EVRP, the agent could be an electric vehicle, and the environment could be a city with charging stations and customer locations. The agent's decisions encompass selecting the most optimal routes and undertaking specific actions. The reward could measure the efficiency and cost-effectiveness of the routes taken. RL can find near-optimal solutions to the EVRP in a more flexible and adaptable way than traditional optimization methods. In this review article, we will discuss the application of RL to the EVRP, the challenges, and opportunities of using RL for this problem and its variants, the current state of the art in RL-based approaches for the EVRP, and directions for future research.