A stochastic optimization model for integrated scheduling of elective, urgent, and emergency surgeries

Operating rooms (ORs) are critical hospital resources where efficiency directly affects profitability and patient access. Community hospitals face a persistent challenge: balancing planned elective procedures against stochastic urgent and emergency arrivals without dedicated emergency ORs, which are economically inefficient at low daily volumes. We propose an integrated two-phase scheduling framework. The offline phase formulates a two-stage stochastic mixed-integer program that determines the optimal daily Total Surgery Time by minimizing expected overtime and postponement costs against historical arrival patterns. The online phase introduces a Minimum Interval Time (MIT) heuristic that dynamically distributes protective slack across the operating day to minimize the maximum idle gap and thereby reduce worst-case emergency waiting times. Simulation experiments calibrated on public surgical data demonstrate that the framework increases net OR utilization by up to 6% compared to fixed schedules and reduces the 90th percentile emergency waiting time from 61 to 39 min. Retrospective out-of-sample validation on an 18-month holdout period confirms that simulated metrics align with historical operational data within 4%. Sophisticated operations management can simultaneously improve clinical outcomes and economic efficiency, challenging the assumption that patient safety and resource utilization must exist in perpetual tension.