Policy Analytics in Public School Operations

Getting students to the right school at the right time can pose a challenge for school districts in the United States, which must balance educational objectives with operational ones, often on a shoestring budget. Examples of such operational challenges include deciding which students should attend, how they should travel to school, and what time classes should start. Froman optimizer's perspective, these decision problems are difficult to solve in isolation, and present a formidable challenge to solve together. In this paper, we develop an optimization-based approach to three key problems in school operations: school assignment, school bus routing, and school start time selection. Our methodology is comprehensive, flexible enough to accommodate a variety of problem specifics, and relies on a tractable decomposition approach. In particular, it comprises a new algorithm for jointly scheduling school buses and selecting school start times that leverages a simplifying assumption of fixed route arrival times, and a postimprovement heuristic to jointly optimize assignment, bus routing, and scheduling. We evaluate our methodology on simulated and real data from Boston Public Schools, with the case study of a summer program for special education students. Using summer 2019 data, we find that replacing the actual student-to-school assignment with our method could lead to total cost savings of up to 8%. A simplified version of our assignment algorithm was used by the district in the summer of 2021 to analyze the cost tradeoffs between several scenarios and ultimately select and assign students to schools for the summer.