Edge-DIRECT: A Deep Reinforcement Learning-based Method for Solving Heterogeneous Electric Vehicle Routing Problem with Time Window Constraints

This trend is particularly evident in the logistics sector, where companies are actively integrating EVs into their transportation fleets. At the heart of this transition is the electric vehicle routing problem (EVRP), an optimization problem central to the operations of these logistics companies, focusing on dealing with the complexities of deploying EVs instead of internal combustion engine vehicles. This article addresses a practical routing problem for EVs, named heterogeneous electric vehicle routing problem with time-window constraints (HEVRPTW). It considers both vehicle attributes, such as varying cargo and battery capacities [4] and customer preferences regarding delivery times [5]. These factors create a more realistic and applicable model for contemporary logistics challenges. HEVRPTW, recognized as an NP-hard optimization problem, seeks to determine a set of routes with minimal cost, total traveling time, or total traveling distance, for a fleet of Heterogeneous EVs to serve each geographically dispersed customer's demands within a specified time-window. Traditional methods, including exact and heuristics solvers, are conventionally employed to solve various vehicle routing problem (VRP) variants. Due to the NP-Hard nature of HEVRPTW, and VRPs in general, exact methods, such as branch-and-price [6] and branchand-price-and-cut [7], consume prohibitively long time for solving practical-size problems [8].