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Friday, November 9, 2018 at 12:15pm
Bike-sharing systems are changing the urban transportation landscape; for example, New York launched the largest bike-sharing system in North America in May 2013, with individual trips exceeding 15 million rides for 2017. We have worked with Citibike, using analytics and optimization to change how they manage the system. Huge rush-hour usage imbalances the system - we answer the following two questions: where should bikes be at the start of a day and how can we mitigate the imbalances that develop?
We will survey the analytics we have employed for the former question, where we developed an approach based on continuous-time Markov chains combined with integer programming models to compute daily stocking levels for the bikes, as well as methods employed for optimizing the capacity of the stations. For the question of mitigating the imbalances that result, we will describe both heuristic methods and approximation algorithms that guide both mid-rush hour and overnight rebalancing, as well as for the positioning of corrals, which have been one of the most effective means of creating adaptive capacity in the system. More recently, we have guided the development of Bike Angels, a program to incentivize users to crowdsource “rebalancing rides”, and we will describe its underlying analytics.
This is joint work with Daniel Freund and Eoin O’Mahony, as well as Hangil Chung, Aaron Ferber, Nanjing Jian, Ashkan Nourozi-Fard, Alice Paul, and David Williamson.
Shane G. Henderson is professor and director (from July 2017) of the School of Operations Research and Information Engineering at Cornell University. He has previously held positions in the Department of Industrial and Operations Engineering at the University of Michigan and the Department of Engineering Science at the University of Auckland. He is the editor in chief of Stochastic Systems. He has served as chair of the INFORMS Applied Probability Society, and as simulation area editor for Operations Research. He is an INFORMS Fellow. His research interests include discrete-event simulation, simulation optimization, and emergency services planning.
David Shmoys obtained his Ph.D. in Computer Science from the University of California at Berkeley in 1984. He has faculty appointments in both the School of Operations Research and Information Engineering and the Department of Computer Science. Shmoys' research has focused on the design and analysis of efficient algorithms for discrete optimization problems. His work has highlighted the central role that linear programming plays in the design of approximation algorithms for NP-hard problems. His current work includes the application of discrete optimization techniques to several issues in computational sustainability, as well as in the development of approximation algorithms for stochastic models of clustering, inventory, and related problems in logistics.