Abstract Globally growing demand for healthcare has highlighted increasing requirements for healthcare management. Healthcare management is complex, and multi-faceted, with many stakeholders, all of whose opinions require consideration. Multi-criteria group decision making is thus necessary for effective healthcare decision making. The aim of this paper is to develop a large-scale group decision making (LSGDM) approach for healthcare management decision-making. Hesitant fuzzy linguistic term sets (HFLTSs) are used to describe the decision information. A clustering method based on the ideal points is proposed to cluster the decision makers (DMs) into several sub-groups. Then DMs’ preferences are fused by possibility distributed extended HFLTSs (PDEHFLTSs) so as to retain as much as decision information as possible. Based on the sub-group size and the proposed hesitant entropy of PDEHFLTSs, a sub-group weighting model is developed to derive the ranking with multiples and interval forms of the sub-group weights. The final weights of sub-groups are then determined by an optimization model which is derived by calculating the shortest distance from the PDEHFLTS positive ideal solution and the farthest distance from the PDEHFLTS negative ideal solution. An example for healthcare management is presented to illustrate the validity of the proposed model.

中文翻译：

---

基于子群加权模型和犹豫模糊语言信息的医疗服务大规模群决策方法

摘要 全球对医疗保健需求的增长凸显了对医疗保健管理的日益增长的要求。医疗保健管理是复杂的、多方面的，涉及许多利益相关者，所有的意见都需要考虑。因此，多标准群体决策对于有效的医疗保健决策是必要的。本文的目的是开发一种用于医疗保健管理决策的大规模群体决策 (LSGDM) 方法。犹豫模糊语言术语集（HFLTS）用于描述决策信息。提出了一种基于理想点的聚类方法，将决策者（DM）聚类为几个子组。然后通过可能性分布式扩展 HFLTS（PDEHFLTS）融合 DM 的偏好，以保留尽可能多的决策信息。基于子组大小和提出的 PDEHFLTS 的犹豫熵，开发了子组权重模型，以得到子组权重的倍数和区间形式的排序。然后由优化模型确定子组的最终权重，该模型通过计算距 PDEHFLTS 正理想解的最短距离和距 PDEHFLTS 负理想解的最远距离而导出。提供了一个医疗保健管理的例子来说明所提出模型的有效性。然后由优化模型确定子组的最终权重，该模型通过计算距 PDEHFLTS 正理想解的最短距离和距 PDEHFLTS 负理想解的最远距离而导出。提供了一个医疗保健管理的例子来说明所提出模型的有效性。然后由优化模型确定子组的最终权重，该模型通过计算距 PDEHFLTS 正理想解的最短距离和距 PDEHFLTS 负理想解的最远距离而导出。提供了一个医疗保健管理的例子来说明所提出模型的有效性。