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.
Raghav Ramesh is a machine learning engineer at DoorDash working on its core logistics engine, where he focuses on core AI problems: vehicle routing, Dasher assignments, delivery time predictions, demand forecasting, and pricing. Previously, Raghav worked on various data products at Twitter, including recommendation systems, trends ranking, and growth analytics. He holds an MS from Stanford University, where he focused on artificial intelligence and operations research.
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