Seminar by
Sakine Batun
Department of Industrial Engineering
Middle East Technical University
Surgery represents a large portion of a typical hospital’s total expenses and revenues. Therefore, operating room (OR) scheduling is an important operational problem for health care delivery systems. These problems are computationally difficult to solve due to not only their combinatorial structure and the existence of multiple resources such as ORs and surgeons, but also the uncertainty in surgery durations.
In this study, we first consider a stochastic multi-OR scheduling problem with multiple surgeons where the daily scheduling decisions are made before the resolution of uncertainty. We formulate the problem as a two-stage stochastic mixed-integer program that minimizes the sum of the fixed cost of opening ORs and the expected overtime and surgeon idling cost. We describe some structural properties of our model and derive a set of widely applicable valid inequalities that make it possible to solve realistic-sized instances. We utilize stage-wise decomposition methods to solve our model, and we use our numerical results to quantify the value of capturing uncertainty and the benefit of pooling ORs, and to demonstrate the impact of parallel surgery processing on surgery schedules.
We then consider a stochastic multi-OR scheduling problem where the initial schedule is revised under perfect information at a prespecified rescheduling point during the surgical day. We use stage-wise and scenario-wise decomposition methods to solve our rescheduling model. By using our results, we estimate the value of rescheduling, and illustrate the impact of different surgery sequencing rules on this value.
This research is joint work with Brian Denton (Department of Industrial and Operations Engineering, University of Michigan), Todd Huschka (Department of Health Care Policy and Research, Mayo Clinic), and Andrew Schaefer (Department of Industrial Engineering, University of Pittsburgh).
Bio: Sakine Batun is an assistant professor in the Department of Industrial Engineering at METU. She received her B.S. and M.S. degrees in Industrial Engineering from METU, and her Ph.D. degree in Industrial Engineering from the University of Pittsburgh. Prior to joining the faculty at METU, she was a postdoctoral fellow at the Richard Ivey School of Business, University of Western Ontario. Her primary research interests are in decision making under uncertainty, with applications in healthcare delivery, medical decision making, production and maintenance scheduling.
