Primary healthcare plays a pivotal role in enhancing health conditions. In Singapore, such services are predominantly manifested through the implementation of the Community Health Assistance Scheme (CHAS). CHAS is an initiative aimed at providing fundamental preventive and therapeutic services, especially for those seniors and low‐income adults with chronic diseases. In spite of considerable efforts in policy and research in this domain, there is a dearth of studies focusing on the spatial optimization of these primary healthcare services. In this study, an innovative multi‐objective medical service facility siting model has been developed based on coarse‐grained parallel genetic algorithm to address the intricate challenges associated with the optimization of locations for CHAS clinics. The proposed optimization model aims to simultaneously maximize accessibility, minimize inequity, and minimize the number of clinics. The successful application of this model in the siting of CHAS clinics in Singapore demonstrates its effectiveness in enhancing residents' access to healthcare services. Apart from its novel academic contributions to the field of spatial optimization of primary healthcare facilities in general, we have also discussed the inherent limitations and identified certain aspects as the future directions of this research.

中文翻译：

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初级卫生保健设施的空间多目标优化：以新加坡为例

初级卫生保健在改善健康状况方面发挥着关键作用。在新加坡，此类服务主要通过实施社区健康援助计划（CHAS）来体现。CHAS 是一项旨在提供基本预防和治疗服务的倡议，特别是为患有慢性病的老年人和低收入成年人。尽管在这一领域的政策和研究方面做出了相当大的努力，但仍缺乏关注这些初级卫生保健服务空间优化的研究。在这项研究中，基于粗粒度并行遗传算法开发了一种创新的多目标医疗服务设施选址模型，以解决与 CHAS 诊所位置优化相关的复杂挑战。所提出的优化模型旨在同时最大化可及性、最小化不平等并最小化诊所数量。该模式在新加坡 CHAS 诊所选址中的成功应用证明了其在提高居民获得医疗服务的机会方面的有效性。除了对初级卫生保健设施空间优化领域的新颖学术贡献外，我们还讨论了其固有的局限性，并确定了某些方面作为本研究的未来方向。