From School Buses to Start Times: Driving Policy With Optimization
Thursday, October 18, 2018 at 4:15pm to 5:15pm
Arthur Delarue & Sebastien Martin
Abstract: Maintaining a fleet of buses to transport students to school is a major expense for U.S. school districts. In order to reduce transportation costs by allowing buses to be reused between schools, many districts spread start times across the morning. However, assigning each school a time involves both 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 are often expensive, inequitable, and even detrimental to student health. For example, medical evidence overwhelmingly indicates that early high school starts are severely 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 and uses mixed-integer optimization to compute and combine subproblem solutions. Our algorithm significantly outperforms state-of-the-art school bus routing methods. The routing engine can be used to formulate 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 the tradeoffs between competing priorities and choose the solution that best suits the needs of the community. Our application in Boston led to $5 million in yearly savings (maintaining service quality despite a 50-bus fleet reduction) and to the unanimous approval of the first school start time reform in thirty years
Bio: Arthur Delarue and Sebastien Martin are PhD candidates at the MIT Operations Research Center. Arthur's interests lie in new applications of mixed-integer optimization and machine learning, with applications in transportation and policy. Sebastien's research is focused on the design of scalable optimization and machine learning algorithms for transportation and public policy. Their ongoing collaboration with Boston Public Schools was featured in the Wall Street Journal.