This AI-powered logistics tool tackles same-day delivery challenges in urban areas. It builds upon SMU's Collaborative Urban Delivery Optimisation (CUDO)  to optimize routes and rider assignments for multiple vendors and eCommerce platforms.  The core features include Hierarchical Optimization to help adapt order dispatching to changing situations, considering factors like distance, overtime, and order status. Reinforcement Learning  AI algorithm recommends jobs to riders that maximize efficiency and earnings.

Benefits from pilot projects show a 20% efficiency improvement and 500 hours saved in delivery time. Enables to measure green footprint from the optimized number of trips made. This technology is applicable to same-day delivery, route optimization, and improving overall logistics efficiency, benefiting companies, riders, and the environment.

Please read more about this technology AI-Based Dynamic Route Optimization And Driver Job Recommendation Tool | Institute of Innovation & Entrepreneurship (smu.edu.sg)

If you are interested in licensing this innovative technology, please contact ktc [at] smu.edu.sg for more details.