Denton Brian, Viapiano James, Vogl Andrea
Mayo Clinic College of Medicine, 200 First St. SW, Rochester, MN 55905, USA.
Health Care Manag Sci. 2007 Feb;10(1):13-24. doi: 10.1007/s10729-006-9005-4.
Operating rooms (ORs) are simultaneously the largest cost center and greatest source of revenues for most hospitals. Due to significant uncertainty in surgery durations, scheduling of ORs can be very challenging. Longer than average surgery durations result in late starts not only for the next surgery in the schedule, but potentially for the rest of the surgeries in the day as well. Late starts also result in direct costs associated with overtime staffing when the last surgery of the day finishes later than the scheduled shift end time. In this article we describe a stochastic optimization model and some practical heuristics for computing OR schedules that hedge against the uncertainty in surgery durations. We focus on the simultaneous effects of sequencing surgeries and scheduling start times. We show that a simple sequencing rule based on surgery duration variance can be used to generate substantial reductions in total surgeon and OR team waiting, OR idling, and overtime costs. We illustrate this with results of a case study that uses real data to compare actual schedules at a particular hospital to those recommended by our model.
手术室（OR）同时是大多数医院最大的成本中心和最大的收入来源。由于手术时长存在显著不确定性，手术室的排班极具挑战性。手术时间长于平均时长不仅会导致下一台手术延迟开始，还可能导致当天其余手术延迟。当当天最后一台手术结束时间晚于预定轮班结束时间时，延迟开始还会产生与加班人员配备相关的直接成本。在本文中，我们描述了一种随机优化模型和一些实用的启发式方法，用于计算应对手术时长不确定性的手术室排班。我们关注手术排序和排班开始时间的同步影响。我们表明，基于手术时长方差的简单排序规则可大幅降低外科医生和手术室团队的总等待时间、手术室闲置时间以及加班成本。我们通过一个案例研究的结果对此进行说明，该案例研究使用实际数据将某家特定医院的实际排班与我们模型推荐的排班进行比较。
