AI 中文总结
该研究针对手术时长不确定的择期手术调度问题，提出鲁棒机会约束框架，通过将不确定性量化与调度优化分离，在医院案例中大幅降低了手术延误与加班时间。
AI 中文摘要
我们研究手术时长不确定情况下的择期手术调度。基于平均时长或固定缓冲规则的调度方案事前看似高效，但执行时却很脆弱，因为早期的时长超支会在当天内传播，使后续手术面临累积延误的风险。我们提出一种鲁棒机会约束框架，该框架将不确定性量化与调度优化分离开来。缓冲引擎将分布信息转换为依赖可靠性的缓冲时长，而调度模型则从离散菜单中共同选择手术分配、顺序、开始时间以及手术级别的可靠性水平。因此，可靠性成为内生的调度决策，而非固定的服务水平参数。该框架可适配平均可靠性、最坏情况日以及硬目标等风险姿态。与通用可靠性和均匀比例缓冲基准的比较显示，该菜单的价值源于利用手术级别的异质性，在具有最大操作价值的地方分配保护，同时避免不必要的保守性。在马德里HLA蒙克洛亚医院的滚动原点案例研究中，涵盖10个实例，涉及45至227台手术，与基于确定性均值的基线相比，该方法将超过90分钟的延误减少了97%，将95百分位的延误降低了约620分钟，并将总加班时间减少了42%。通过根据不确定性和操作暴露分配缓冲，该框架将异质时长数据和风险偏好转化为执行时更可靠且比一刀切规则缓冲更少保守性的调度方案。
英文摘要
We study elective surgery scheduling under uncertain procedure durations. Schedules based on mean durations or fixed buffering rules may appear efficient ex ante but become fragile in execution, as early overruns propagate through the day and expose later surgeries to accumulated delay. We propose a robust chance-constrained framework that separates uncertainty quantification from schedule optimization. A buffer engine converts distributional information into reliability-dependent buffered durations, while the scheduling model jointly selects assignments, sequences, start times, and surgery-level reliability levels from a discrete menu. Reliability therefore becomes an endogenous scheduling decision rather than a fixed service-level parameter. The framework accommodates average-reliability, worst-day, and hard-target risk postures. Comparisons with common-reliability and uniform proportional-buffer benchmarks show that the menu derives its value from exploiting surgery-level heterogeneity, allocating protection where it has the greatest operational value while avoiding unnecessary conservatism. In a rolling-origin case study at HLA Moncloa Hospital in Madrid, covering 10 instances with 45 to 227 surgeries, the approach reduces delays exceeding 90 minutes by 97%, lowers the 95th-percentile delay by approximately 620 minutes, and reduces total overtime by 42% relative to a deterministic mean-based baseline. By allocating buffers according to uncertainty and operational exposure, the framework translates heterogeneous duration data and risk preferences into schedules that are more reliable in execution and less conservatively buffered than one-size-fits-all rules.
Comments38 pages, 7 figures, 11 tables
