Many problems faced by US school districts present interesting challenges for operations research practitioners. In this talk, we focus on the two major problems of school transportation and bell time choice, as part of a collaboration with Boston Public Schools (BPS), the oldest and one of the largest school districts in the nation (with 126 public schools and over 56,000 students). For the problem of school transportation, which involves delivering students to school every morning and back home every afternoon using a fleet of specialized vehicles (around 700 hundred school buses for Boston), we identify a natural multistage decomposition of the problem and propose integer programming formulations and efficient heuristics for each stage to compute school bus routes at the scale of the district. For the problem of bell time choice, we develop an integer optimization model with multiple objectives that provides both the versatility and simplicity that is required to facilitate public policy decision-making . Above all, the talk highlights interesting aspects of a collaboration with a public organization and describes the process of implementing OR solutions at the scale of a major city.

Arthur Delarue is a PhD student at the MIT Operations Research Center, working on mixed-integer optimization and its applications to real-world transportation problems under the supervision of Prof. Bertsimas.

Sebastien Martin is a PhD student at the MIT Operations Research Center, working on tractable large scale optimization with a focus on routing optimization and machine learning, under the supervision of Profs. Dimitris Bertsimas and Patrick Jaillet.

Edoardo M. Airoldi (airoldi@alumni.harvard.edu) is an Associate Professor of Statistics at Harvard University, where he has been directing the Harvard Laboratory for Applied Statistical Methodology & Data Science since 2009. He holds a Ph.D. in Computer Science and an M.Sc. in Statistics from Carnegie Mellon University, and a B.Sc. in Mathematical Statistics and Economics from Bocconi University. His current research focuses on statistical theory and methods for designing and analyzing experiments on large networks, and on modeling and inferential issues that arise in analyses that leverage network data. His work has appeared in journals across statistics, computer science and engineering, including Annals of Statistics, Journal of the American Statistical Association, Journal of Machine Learning Research, Proceedings of the National Academy of Sciences, and Nature. He is the recipient of several research and young investigator awards including an Alfred Sloan Research Fellowship and a Shutzer Fellowship from the Radcliffe Institute of Advanced Studies. He delivered an IMS Medallion Lecture at JSM 2017 in Baltimore.

Marta C. Gonzalez is Associate Professor of City and Regional Planning at the University of California, Berkeley, and a Physics Research faculty in the Energy Technology Area (ETA) at the Lawrence Berkeley National Laboratory (Berkeley Lab). With the support of several companies, cities and foundations, her research team develops computer models to analyze digital traces of information mediated by devices. They process this information  to manage the demand in urban infrastructures in relation to energy and mobility. Her recent research uses billions of mobile phone records to understand the appearance of traffic jams and the integration of electric vehicles into the grid, smart meter data records to compare the policy of solar energy adoption and card transactions. Credit to identify habits in spending behavior. Prior to joining Berkeley, Marta worked as an Associate Professor of Civil and Environmental Engineering at MIT, a member of the Operations Research Center and the Center for Advanced Urbanism. She is a member of the scientific council of technology companies such as Gran Data, PTV and the Pecan Street Project consortium.