Sequential three-way group decision-making for double hierarchy hesitant fuzzy linguistic term set

TL;DR

This paper introduces a novel multi-level sequential three-way decision method for group decision-making under double hierarchy hesitant fuzzy linguistic environments, enhancing efficiency and interpretability in complex uncertain scenarios.

Contribution

It proposes a new multi-level expert information fusion approach and a sequential three-way decision framework for DHHFLTS, addressing efficiency and interpretability issues in GDM.

Findings

The method improves decision-making efficiency in complex GDM scenarios.

It effectively fuses multi-level expert information with high interpretability.

Experimental results demonstrate the method's superiority over existing approaches.

Abstract

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.