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Friday, January 11, 2019 at 11:00am
Maintaining a fleet of buses to transport students to school is a major expense for U.S. school districts. In order to reduce costs by reusing buses between schools, many districts spread start times across the morning. However, assigning each school a time involves estimating the impact on transportation costs and reconciling additional competing objectives. Facing this intricate optimization problem, school districts must resort to ad hoc approaches, which can be expensive, inequitable, and even detrimental to student health. For example, there is medical evidence that early high school starts are impacting the development of an entire generation of students and constitute a major public health crisis. We present the first algorithm to jointly optimize school bus routing and bell time assignment. Our method leverages a natural decomposition of the routing problem, computing and combining subproblem solutions via mixed-integer optimization. It significantly outperforms state-of-the-art routing methods, and its implementation in Boston has led to $5 million in yearly savings, maintaining service quality for students despite a 50-bus fleet reduction. With the routing engine, we construct a tractable proxy to transportation costs, which allows the formulation of the bell time assignment problem as a multi-objective Generalized Quadratic Assignment Problem. Local search methods provide high-quality solutions, allowing school districts to explore tradeoffs between competing priorities and choose times that best fulfill community needs. In December 2017, the development of this method led the Boston School Committee to unanimously approve the first school start time reform in thirty years. In 2018, our collaboration with media and policy specialists generated an intense debate in Boston and many other cities on the use of OR tools for social good.
Sébastien Martin is a Ph.D. candidate in Operations Research at MIT, advised by Prof. Dimitris Bertsimas and Patrick Jaillet. Beforehand, he obtained a M.Sc. and B.Sc. from École Polytechnique in France. He has worked as a software engineering intern at Google Maps. His research focuses on large scale optimization, with applications in machine learning and transportation, and an emphasis on implementation and policy. His recent work, covered by the Wall Street Journal, the Boston Globe and Wired, led to major policy changes and millions of dollars in yearly saving for Boston and is a 2019 Edelman Award finalist.