Spotlight on Data: How DoorDash Solved the Global Optimality Problem with Gary Ren

The common algorithmic challenge for the current crop of marketplace companies 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.

At its core, this marketplace logistics problem is a core operations research problem called the vehicle routing problem. But DoorDash faces additional challenges: delivery requests come in in real time; most orders need to be delivered immediately; Dashers are in constant 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 intractable. To address these challenges, DoorDash leverages various AI techniques to intelligently model the decision space and achieve near-optimal solutions in seconds.

In this Spotlight on Data, DoorDash’s Gary Ren explains how his team defined the problem of modeling the marketplace and then implemented machine learning techniques to solve the marketplace matching problem.

Recorded on December 11, 2019. See the original event page for resources for further learning or watch recordings of other past events.

O’Reilly Spotlight explores emerging business and technology topics and ideas through a series of one-hour interactive events. In live conversations, participants share their questions and ideas while hearing the experts’ unique perspectives, insights, fears, and predictions for the future.

In every edition of Spotlight on Data, you’ll learn about, discuss, and debate the tools, techniques, questions, and quandaries in the world of data. You’ll discover how successful companies leverage data effectively and how you can follow their lead to transform your organization and prepare for the Next Economy.