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Dr. Lian Zhen
Postdoctoral fellow
Lyft Rideshare Labs

ABSTRACT

Motivated by a collaboration with the San Francisco Unified School District (SFUSD), this paper presents an interactive optimization framework for addressing complex public policy problems. These problems suffer from a chicken-and-egg dilemma, where policymakers understand the objectives and constraints but lack the ability to solve them (“the optimization problem”), while researchers possess the necessary algorithms but lack the necessary insights into the policy context (“the policy problem”). Our framework addresses this challenge by combining three key elements: (1) an efficient optimization algorithm that can solve the problem given certain known objectives, (2) a method for generating a large set of diverse, near-optimal solutions, and (3) an interface that facilitates exploration of the solution space. We illustrate the effectiveness of this framework by applying it to the problem of improving school schedules at SFUSD. The resulting schedule, implemented in August 2021, saved the district over $5 million and, to our knowledge, represents the first successful optimization-driven school start time change in the United States.