PI: Dr. Alaa Khamis
CoI: Dr. Anas Al-Ghazi, Dr. Slim Belhaiza, Dr. Mohammed Abdulaal, Dr. Haitham Saleh

This project addresses the eco-efficient and adaptive routing problem during live-heading and deadheading states of delivery vehicles, including trucks, cars, cargo bikes, and motorcycles. The problem is treated as a multi-criteria constrained dynamic routing problem, combining real-time and historical data to identify the most eco-efficient routes. Advanced AI search algorithms will be used to explore this complex logistics optimization problem and find near-optimal solutions efficiently. Both mathematical and data-driven models will be investigated in this project to estimate CO2 reductions compared to conventional navigation apps.