Operating Room Scheduling in Teaching Hospitals: A Novel Stochastic Model

Operating room scheduling is key to optimal operating room productivity. The significant uncertainty associated with surgery duration renders scheduling of surgical operation a challenging task. This paper proposes a novel Stochastic Operating Room Scheduling (SORS) to schedule surgery cases with uncertain durations. The model considers various surgical operation constraints in teaching hospital, including assignment of residents for each surgery under attend supervision, chronologic curriculum plan for training residents, balanced distribution of operations between surgeons, and the infection prevention constraints. A two-stage mixed-integer programming model (SORS), with the total operating rooms idle time and over time as the objective function was developed. The L-shaped algorithm was used for solving the programming model. The performance of the SORS in proposing optimal scheduling solutions was extensively compared with that of deterministic models as well as the performance of manual scheduling obtained from clinical data. Our results showed that SORS offers higher performance in proposing operating room scheduling solutions as compared with the deterministic computational approaches. Furthermore, comparison of the SORS-proposed schedules with the practical schedules indicated that SORS can remarkably reduce the operating room idle time and over time, demonstrating the utility of this model in clinical practice. A novel validated computational operating room scheduling model was developed which can potentially be employed to achieve higher operating room efficiency.