Machine Learning Applied to Electric Vehicle Routing Problem: Optimizing Costs for a Sustainable Environment

Abstract

The global move towards Electric Vehicles (EVs) marks a crucial step towards sustainable transportation. However, effectively integrating EVs into the current infrastructure demands more than technological advancements. One of the key challenges is optimizing the routing of EVs to minimize costs and environmental impact. This editorial examines the role of Machine Learning (ML) in addressing the electric vehicle routing problem (ESVRP), highlighting its potential to transform cost optimization and sustainability in transportation. Routing is a fundamental part of transportation logistics, influencing efficiency, cost, and environmental impact. While traditional internal combustion engine vehicles have established routing systems, EVs present unique challenges such as limited battery capacity, longer refueling times, and fewer charging stations. These factors require advanced routing solutions that can dynamically adapt to various constraints.