We develop a novel, data-driven approach to deal with appointment sequencing and scheduling in a multi-server system, where both customer punctuality and service times are stochastic. Our model is calibrated using a data set of unprecedented resolution, gathered at a large-scale outpatient oncology practice. This data set combines real-time locations, electronic health records and appointments logs. Our approach yields tractable and scalable solutions that accommodate hundreds of jobs and servers. We demonstrate the performance of our algorithm by comparing it with existing state-of-the-art sequencing and scheduling algorithms.

Bio: Petar Momcilovic is an associate professor in the Department of Industrial and Systems Engineering at the University of Florida. His research interests are in the domain of stochastic modeling and applied probability. He received the PhD degree in Electrical Engineering from Columbia University. His research has been supported by NSF, NIH and IBM Research.