Abstract

In this article, we address a two-stage scheduling problem that requires coordination between clinical and surgical appointments for specialized surgeries. First, patients have a clinical appointment with a surgeon to determine whether they are an appropriate candidate for the surgical procedure. Subsequently, if the decision to pursue the surgery is made the patient undergoes the procedure on a later date. However, the scheduling process aims to book both the clinical and surgical appointments for a patient at the time of the initial appointment request. Two sources of uncertainty make this scheduling process challenging: (i) the patient may or may not need surgery after the clinical appointment and (ii) the surgery duration for each patient and procedure is unknown. We present a Distributionally Robust Optimization (DRO) approach for coordinating clinical and surgical appointments under these uncertainties. A case study of the Transcatheter Aortic Valve Replacement procedure at Mayo Clinic, Rochester, MN is presented. Numerical results include comparisons with the current practice and four heuristic scheduling policies from the literature. Results show that the DRO-based scheduling policies lead to lower total surgeon idle-time and overtime per day. The proposed policies also restrict the under and over utilization of clinical capacity.

摘要

在本文中，我们解决了一个两阶段调度问题，该问题需要在专科手术的临床和外科预约之间进行协调。首先，患者与外科医生进行临床预约，以确定他们是否适合进行外科手术。随后，如果决定进行手术，患者将在以后接受手术。然而，调度过程旨在在初始预约请求时为患者预约临床和手术预约。两个不确定性来源使这个调度过程具有挑战性：(i) 患者在临床预约后可能需要也可能不需要手术，以及 (ii) 每位患者的手术持续时间和程序是未知的。我们提出了一种分布式稳健优化 (DRO) 方法，用于在这些不确定性下协调临床和手术预约。介绍了明尼苏达州罗彻斯特梅奥诊所的经导管主动脉瓣置换手术案例研究。数值结果包括与当前实践和文献中的四种启发式调度策略的比较。结果表明，基于 DRO 的调度策略可减少外科医生每天的总空闲时间和加班时间。拟议的政策还限制了临床能力的不足和过度利用。数值结果包括与当前实践和文献中的四种启发式调度策略的比较。结果表明，基于 DRO 的调度策略可减少外科医生每天的总空闲时间和加班时间。拟议的政策还限制了临床能力的不足和过度利用。数值结果包括与当前实践和文献中的四种启发式调度策略的比较。结果表明，基于 DRO 的调度策略可减少外科医生每天的总空闲时间和加班时间。拟议的政策还限制了临床能力的不足和过度利用。