Group decision-making (GDM) characterized by complexity and uncertainty is an essential part of various life scenarios. Most existing researches lack tools to fuse information quickly and interpret decision results for partially formed decisions. This limitation is particularly noticeable when there is a need to improve the efficiency of GDM. To address this issue, a novel multi-level sequential three-way decision for group decision-making (S3W-GDM) method is constructed from the perspective of granular computing. This method simultaneously considers the vagueness, hesitation, and variation of GDM problems under double hierarchy hesitant fuzzy linguistic term sets (DHHFLTS) environment. First, for fusing information efficiently, a novel multi-level expert information fusion method is proposed, and the concepts of expert decision table and the extraction/aggregation of decision-leveled information based on the multi-level granularity are defined. Second, the neighborhood theory, outranking relation and regret theory (RT) are utilized to redesign the calculations of conditional probability and relative loss function. Then, the granular structure of DHHFLTS based on the sequential three-way decision (S3WD) is defined to improve the decision-making efficiency, and the decision-making strategy and interpretation of each decision-level are proposed. Furthermore, the algorithm of S3W-GDM is given. Finally, an illustrative example of diagnosis is presented, and the comparative and sensitivity analysis with other methods are performed to verify the efficiency and rationality of the proposed method.

翻译：具有复杂性和不确定性的群决策是各种生活场景中的重要组成部分。现有研究大多缺乏对部分形成决策进行快速信息融合和解释决策结果的工具。当需要提高群决策效率时，这一局限性尤为明显。为解决该问题，本文从粒计算视角构建了一种新颖的多层次序贯三支群决策方法。该方法同时考虑了双层级犹豫模糊语言术语集环境下群决策问题的模糊性、犹豫性和变异性。首先，为高效融合信息，提出了一种新颖的多层次专家信息融合方法，定义了专家决策表的概念以及基于多层次粒度的决策层级信息提取/聚合机制。其次，利用邻域理论、优劣关系和后悔理论重新设计了条件概率与相对损失函数的计算方法。随后，定义了基于序贯三支决策的双层级犹豫模糊语言术语集粒度结构以提升决策效率，并提出了各决策层级的决策策略与解释机制。此外，给出了序贯三支群决策的算法流程。最后，通过诊断案例进行演示，并与其他方法进行对比分析和敏感性分析，验证了所提方法的有效性与合理性。