Various medical and surgical units operate in a typical hospital and to treat their patients these units compete for infrastructure like operating rooms (OR) and ward beds. How that competition is regulated affects the capacity and output of a hospital. This article considers the impact of treating different patient case mix (PCM). As each case mix has an economic consequence and a unique profile of hospital resource usage, this consideration is important. To better understand the case mix landscape and to identify those which are optimal from a capacity utilisation perspective, an improved multicriteria optimization (MCO) approach is proposed. As there are many patient types in a typical hospital, the task of generating an archive of non-dominated (i.e., Pareto optimal) case mix is computationally challenging. To generate a better archive, an improved parallelised epsilon constraint method (ECM) is introduced. Our parallel random corrective approach is significantly faster than prior methods and is not restricted to evaluating points on a structured uniform mesh. As such we can generate more solutions. The application of KD-Trees is another new contribution. We use them to perform proximity testing and to store the high dimensional Pareto frontier (PF). For generating, viewing, navigating, and querying an archive, the development of a suitable decision support tool (DST) is proposed and demonstrated.

中文翻译：

---

用于了解医院病例组合情况的多标准优化技术


典型的医院内设有各种医疗和外科单位，为了治疗患者，这些单位会争夺手术室 (OR) 和病床等基础设施。如何监管竞争会影响医院的能力和产出。本文考虑了治疗不同患者病例组合 (PCM) 的影响。由于每种病例组合都会产生经济后果和医院资源使用的独特情况，因此这一考虑很重要。为了更好地理解案例组合景观并从容量利用率的角度识别最佳组合，提出了一种改进的多标准优化（MCO）方法。由于典型医院中有许多患者类型，因此生成非支配（即帕累托最优）病例组合档案的任务在计算上具有挑战性。为了生成更好的存档，引入了改进的并行 epsilon 约束方法 (ECM)。我们的并行随机校正方法比以前的方法要快得多，并且不限于评估结构化均匀网格上的点。因此我们可以产生更多的解决方案。 KD-Trees 的应用是另一个新的贡献。我们使用它们来执行邻近测试并存储高维帕累托前沿（PF）。为了生成、查看、导航和查询档案，提出并演示了合适的决策支持工具（DST）的开发。