The Benefit of Pooling Operating Rooms and Parallel Surgery Processing under Uncertainty

Operating room (OR) scheduling is an important operational problem for most hospitals. In this study, we present a novel two-stage stochastic mixed-integer programming (SMIP) model to minimize total expected operating cost given that scheduling decisions are made before the resolution of uncertainty in surgery durations. By using our model, we quantify the benefit of pooling ORs as a shared resource and the value of parallel surgery processing. Decisions in our model include the number of ORs to open each day, the allocation of surgeries to ORs, the sequence of surgeries within each OR, and the start time for each surgeon. Realisticsized instances of our model are difficult or impossible to solve with standard stochastic programming techniques. Therefore, we exploit several structural properties of the model to achieve computational advantages. Furthermore, we describe a novel set of widely applicable valid inequalities that make it possible to solve practical instances. Based on our results for different resource usage schemes, we conclude that the benefit of pooling ORs and parallel surgery processing lead to total cost reductions as high as 60% and 80%, respectively.