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Title:
A Practical Framework for Real-time Assignment Optimization: Perspectives from DoorDash

Abstract:
While the field of delivery logistics has been well studied in academia and industry, we found the common methodologies used to optimize these systems less applicable to improving the efficiency of DoorDash’s real-time last-mile logistics platform. These common methodologies require a stable prototype environment that is difficult to build in our platform and does not allow for the accurate measurement of the algorithm change. To address our specific use case, we designed an experiment-based framework that allows us to rapidly iterate our algorithms and accurately measures the impact of every algorithm change.

Bio:
Sifeng Lin works as operations research scientist in DoorDash. In this role, he combines operations research and software engineering to solve the real-time dispatching problem, as well as tackling challenging optimization problems in other frontier of DoorDash business. He has a PhD in Operations Research from the University of Texas at Austin and previously worked as a Sr. operations research specialist in BNSF Railway.