Efficient route planning plays a critical role in modern delivery and logistics systems, especially with the increasing demand for fast and sustainable transportation services. Traditional route planning approaches often lead to inefficient delivery assignments, increased travel distance, and higher fuel consumption due to poor workload distribution among delivery personnel. To address these challenges, this paper presents GreenRoute AI, an intelligent route optimization system that organizes delivery locations into optimized clusters and assigns them to delivery personnel in an efficient manner. The proposed system processes route and location data, applies clustering and optimization techniques to minimize travel distance and balance delivery workloads, and presents the optimized results through an interactive dashboard. The dashboard enables real-time visualization of delivery clusters, optimized routes, and performance metrics, supporting effective decision-making for delivery management. Experimental evaluation demonstrates that the proposed approach significantly reduces total travel distance and improves delivery efficiency when compared to unoptimized routing methods. The results indicate that GreenRoute AI offers a scalable and eco-friendly solution for smart logistics and last-mile delivery optimization.
Khemnar et al. (Tue,) studied this question.