operating room scheduling in teaching hospitals: a novel stochastic optimization model

background and objectives: operating room (or) 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 computational stochastic model to optimize scheduling of surgeries with uncertain durations. the model considers various surgical operation constraints in teaching hospitals, including optimal of assigning surgeons, residents, and assistant surgeons to each surgery, infection prevention constraints, availability of surgeons, and balanced distribution of operations between various groups of surgeons. methods: a two-stage stochastic operating room scheduling (sors) framework was developed to minimize idle time and over time of ors under practical constraints. the optimization model was solved using l-shaped algorithm. 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. findings: results from implication of model on sample real-life or scheduling problems showed that sors offers more efficient scheduling solutions as compared with the corresponding deterministic model. furthermore, comparison of the sors-proposed schedules with the practical schedules indicated that sors can remarkably reduce the operating room idle times (96%) and overtimes (87%), suggesting the utility of this model in clinical practice. conclusions: a novel validated computational operating room scheduling model was developed, which can potentially be employed to achieve higher operating room performance.