How DoorDash leverages AI in its world-class on-demand logistics engine

Description

Recent years have seen a surge in marketplace companies where the common algorithmic challenge is efficient matching between two sides of the marketplace. For DoorDash, which focuses on food delivery, this problem is even more difficult because of its three-sided marketplace, where the company must identify the optimal Dasher to fulfill a delivery from a restaurant and bring it to a consumer. Raghav Ramesh highlights AI techniques used by DoorDash to enhance efficiency and quality in its marketplace and provides a framework for how AI can augment core operations research problems like the vehicle routing problem (VRP).

VRP in its simplest form is NP hard, and the real-time, quick-turnaround nature of DoorDash introduces additional challenges: delivery requests come in continuously, Dashers constantly are in movement, and the effects of variance in restaurant operations and real-world events (traffic, weather, etc.) have pronounced effects on the solutions. Thus, finding global optimality in real time becomes further intractable. DoorDash leverages various AI techniques to intelligently model the decision space and achieve near optimal solutions in seconds. Ultimately, across DoorDash’s tens of millions of deliveries, these techniques have led to shorter delivery times for consumers, higher pay for Dashers, increased income for merchant partners, and a better experience for all sides of the marketplace.

Topics include:

• An overview of DoorDash’s AI techniques
• Predicting time points in the life of a delivery: How predictions are made and, more importantly, how they fit in with the larger logistics engine
• ML techniques for batching multiple deliveries together: Algorithms used in the logistics engine to decide when and how deliveries can be batched together