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.

中文翻译：

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一种基于多目标 [公式省略] 约束的方法，用于在多重不确定性下稳健的主手术计划


医院择期手术的高效安排对于确保患者满意度、成本效益和整体运营效率至关重要。然而，由于不确定性来源众多，以及拟议的时间表对下游恢复病房（如重症监护病房 （ICU））的影响，手术室 （OT） 经理面临着复杂且相互竞争的调度问题。为了应对这些挑战，本研究为多重不确定性下的每周主手术计划 （MSS） 开发了一个多目标稳健的计划模型。该模型考虑了患者优先级、分配成本和工作量平衡，同时还考虑了 OT 的限制、外科医生的可用性、下游资源以及手术持续时间和患者在 ICU 住院时间 （LOS） 的不确定性。为了评估稳健解，使用蒙特卡洛模拟来计算约束违规的风险，并将适应性ε约束算法用于四目标问题，以计算帕累托前沿并计算每个不确定性程度的超体积。这为 OT 决策者提供了一个全面的决策工具，并允许根据计划患者数量、取消患者和 OT 利用率来比较各种场景。