The problem under study is based on the challenges faced by the Orthopaedic Clinic at St. Olav’s Hospital in Trondheim, Norway. Variations in demand and supply cause fluctuating waiting lists, and it is challenging to level the activities between the clinic’s two units, the outpatient clinic and the operating theater, to obtain short waiting times for all activities. Based on these challenges, we describe and present a planning problem referred to as the Long-term Master Scheduling Problem (LMSP), where the objective is to construct an integrated Long-term Master Schedule (LMS) that facilitates short waiting times in both units. The LMS can be separated into two schedules, one cyclic high-level schedule, and one non-cyclic low-level schedule. The demand for outpatient clinic consultations and surgeries is stochastic, as are the waiting lists. To account for this, we propose a planning framework consisting of an optimization model to solve the LMSP, and a two-level planning procedure. In the planning procedure, we first solve the LMSP to construct the LMS for the upcoming planning horizon. Then, to adjust to the fluctuating waiting lists, we periodically refine the low-level schedule by solving a constrained LMSP. We also develop a simulation-based evaluation procedure to evaluate the planning framework in a real-life setting and use this to investigate different planning strategies. We find that imposing flexible, dynamic and agile planning strategies improve waiting time outcomes and patient throughput. Furthermore, combining the strategies yields additive improvements.

中文翻译：

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外科诊所综合资源规划框架


正在研究的问题是基于挪威特隆赫姆圣奥拉夫医院骨科诊所面临的挑战。需求和供应的变化导致等候名单波动，并且很难平衡诊所两个单位（门诊诊所和手术室）之间的活动，以缩短所有活动的等待时间。基于这些挑战，我们描述并提出了一个称为长期主调度问题（LMSP）的规划问题，其目标是构建一个集成的长期主调度（LMS），以缩短两个单元的等待时间。 LMS 可以分为两种调度，一种是循环高级调度，一种是非循环低级调度。门诊咨询和手术的需求是随机的，等候名单也是随机的。为了解决这个问题，我们提出了一个规划框架，其中包括一个求解 LMSP 的优化模型和一个两级规划程序。在规划过程中，我们首先求解 LMSP，为即将到来的规划范围构建 LMS。然后，为了适应波动的等待列表，我们通过求解约束 LMSP 定期完善低级时间表。我们还开发了基于模拟的评估程序来评估现实生活中的规划框架，并用它来研究不同的规划策略。我们发现，实施灵活、动态和敏捷的规划策略可以改善等待时间结果和患者吞吐量。此外，结合这些策略可以产生额外的改进。