Stochastic Optimization Approaches for an Operating Room and Anesthesiologist Scheduling Problem
提出随机规划和分布鲁棒优化方法，解决多手术室、多麻醉医生和多种手术类型在手术时长不确定下的集成分配、指派、排序与调度问题，并用纽约医疗系统真实数据验证了计算效率与实践价值。
Improving Operating Room, Surgery, and Anesthesiologist Scheduling Using Stochastic and Robust Optimization Efficient planning and scheduling of operating room (OR) activities is crucial for managing costs and delivering high-quality surgical care. However, this task is extremely complex for several reasons. First, it requires coordinating multiple resources, such as ORs and anesthesiologists. Second, in addition to limited OR capacity and time, there is a significant shortage of anesthesiologists required to perform surgeries. Third, surgery durations are uncertain and difficult to predict. Ignoring such uncertainty may lead to substantial overtime, idling, and surgery delays, among other schedule deficiencies. Thus, hospital managers could benefit greatly from advanced methodologies to improve OR utilization, surgical care, and quality as well as to minimize OR operational costs. In “Stochastic Optimization Approaches for an Operating Room and Anesthesiologist Scheduling Problem,” M. Y. Tsang, K. S. Shehadeh, F. E. Curtis, B.R. Hochman, and T. E. Brentjens propose computationally tractable stochastic programming and distributionally robust optimization methodologies for an integrated allocation, assignment, sequencing, and scheduling problem under uncertainty involving multiple ORs, anesthesiologists, and surgery types. Using real-world surgery data and a case study from a health system in New York, they conduct extensive experiments demonstrating the computational efficiency of the proposed methodologies, allowing for their implementation in practice. Moreover, they show the negative consequences of adopting the existing non-integrated approaches and provide valuable practical insights.