A multiobjective ϵ-constraint based approach for the robust master surgical schedule under multiple uncertainties

The efficient scheduling of elective surgeries in hospitals is critical for ensuring patient satisfaction, cost-effectiveness, and overall operational efficiency. However, operating theater (OT) managers face complex and competing scheduling problems due to numerous sources of uncertainty and the impact of the proposed schedule on downstream recovery units, such as the intensive care unit (ICU). To address these challenges, this study develops a multiobjective robust planning model for the weekly Master Surgical Schedule (MSS) under multiple uncertainties. The model takes into account patient priority, assignment cost and workload balancing, while also considering the constraints of the OT, surgeon availabilities, downstream resources, and the uncertainty of surgery duration and patients’ length of stay (LOS) in the ICU. To evaluate the robust solutions, a Monte Carlo simulation is used to calculate the risk of constraint violations, and an adapted $ϵ$-constraint algorithm is used for the four-objective problem to compute the Pareto front and calculate the hypervolume for every degree of uncertainty. This provides a comprehensive decision tool for OT decision makers and allows for the comparison of various scenarios in terms of the number of scheduled patients, canceled patients, and the utilization rate of the OT.