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Wednesday, December 9 2020
4:00pm - 5:00pm
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ISyE Seminar-Arthur J Delarue

Title: 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. From an 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 the simplifying assumption of fixed route arrival times to allow for multiple objectives and enhance tractability. We show that our methodology can significantly streamline the operations of Boston Public Schools Extended School Year (ESY) 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%. Our models can also be used to quantify the costs and benefits of particular operational policies, providing administrators with an analytics framework to evaluate potential decisions.


Arthur Delarue is a fifth-year PhD student in the MIT Operations Research Center, advised by Dimitris Bertsimas. His primary aim as a researcher is to leverage data, optimization, and machine learning, in order to solve practical problems that matter to society. In particular, he is interested in applications of mixed-integer optimization in transportation, machine learning, educational operations and public policy. He is a recipient of the MIT ORC Best Student Paper Award and the William Pierskalla Best Paper Award, as well as a Franz Edelman Laureate.