Sequential Three-Way Group Decision-Making for Double Hierarchy Hesitant Fuzzy Linguistic Term Set

作     者：Luo, Nanfang Zhang, Qinghua Xie, Qin Wang, Yutai Yin, Longjun Wang, Guoyin 

作者机构：Chongqing Key Laboratory of Tourism Multisource Data Perception and Decision Ministry of Culture and Tourism Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China 

出 版 物：《SSRN》 

年 卷 期：2024年

核心收录：

主　　题：Information fusion 

摘      要：Group decision-making (GDM) characterized by complexity and uncertainty is an essential part of various life *** research lacks tools to fuse information quickly and interpret decision results for partially formed *** limitation is particularly noticeable when there is a need to improve the efficiency of *** address this issue, a novel multi-level sequential three-way decision for group decision-making (S3W-GDM) model is constructed via granular *** model considers the vagueness, hesitation, and variation in the double hierarchy hesitant fuzzy linguistic term sets (DHHFLTS) GDM ***, for fusing information efficiently, a novel multi-level expert information fusion method is proposed, and the expert decision table and the extraction/aggregation of decision-leveled information based on the multi-level granularity are ***, the neighborhood theory, outranking relation and regret theory (RT) are utilized to redesign the calculations of conditional probability and relative loss function at each ***, the granular structure of DHHFLTS based on sequential three-way decision (S3WD) is defined, and the decision-making strategy and interpretation of decision-level are proposed. Furthermore, the algorithm of S3W-GDM model is ***, an illustrative example are presented, and the comparative and sensitivity analyses are performed to verify the superiority of this model. © 2024, The Authors. All rights reserved.