DeliverAI: Reinforcement Learning Based Distributed Path-Sharing Network for Food Deliveries

Ashman Mehra, Snehanshu Saha, Vaskar Raychoudhury, Archana Mathur

Abstract: The Online Food Delivery (OFD) industry, propelled by the recent pandemic, has witnessed substantial growth in the last decade. Major players like Amazon Fresh, GrubHub, UberEats, Postmates, InstaCart, and DoorDash share a common food delivery business model. However, existing methods lack efficiency as deliveries are individually optimized or bundled inefficiently. Recognizing the potential for cost reduction, we model our food delivery problem as a multi-objective optimization, focusing on consumer satisfaction and delivery costs. Taking inspiration from ride-sharing taxis and the prevalent order bundling, we propose DeliverAI - a reinforcement learning-based path-sharing algorithm. Our novel agent interaction scheme dynamically groups deliveries going in the same direction to reduce the total distance traveled while keeping a satisfactory delivery completion time. We test DeliverAI vigorously on a simulation setup using real data from the city of Chicago. Our results show that DeliverAI can reduce the delivery fleet size by 12%, the distance traveled by 13%, and achieve 50% higher fleet utilization compared to the baselines.