A deficiency of the postanesthesia care unit (PACU) beds or nurses may cause delays in the operating rooms (ORs) and increase the number of cancellations. Some disruptions like the COVID-19 pandemic may cause this deficiency. This paper investigates two integrated OR and PACU scheduling problems; one with few PACU beds, and the other with few PACU nurses. For each problem, a mathematical model and a matheuristic are proposed for minimising the number of cancellations. To the best of our knowledge, it is the first study that investigates the implications of a severe lack of the PACU beds or nurses on the number of cancellations. The matheuristics hybridise the decomposition of each instance into some small-sized sub-instances with a variable neighbourhood search algorithm. The main advantages of these methods are their flexibility to incorporate many problem details (such as a step-wise demand for the PACU nurses) and to solve any large-scale problem. Numerical results for a data set with 22 ORs show that with an increasingly severe lack of PACU capacity there is progressively greater benefit of the matheuristics than their initial solutions. Moreover, these results show the influence of the overtime and the recovery in ORs on improving the situation.

麻醉后护理病房 (PACU) 床位或护士的不足可能会导致手术室 (OR) 延误并增加取消手术的次数。像 COVID-19 大流行这样的中断可能会导致这种缺陷。本文研究了两个集成的 OR 和 PACU 调度问题；一个有几个PACU床位，另一个有几个PACU护士。对于每个问题，都提出了一个数学模型和一个数学模型来最小化取消的数量。据我们所知，这是第一项调查严重缺乏 PACU 床位或护士对取消数量的影响的研究。数学将每个实例的分解混合成一些具有可变邻域搜索算法的小型子实例。这些方法的主要优点是它们可以灵活地结合许多问题细节（例如对 PACU 护士的逐步需求）并解决任何大规模问题。具有 22 个 OR 的数据集的数值结果表明，随着 PACU 容量的日益严重不足，与最初的解决方案相比，数学的好处越来越大。此外，这些结果显示了加班和手术室恢复对改善情况的影响。