Consensus efficiency in group decision making: A comprehensive comparative study and its optimal design

The hesitant fuzzy linguistic term set (HFLTS) has gained great success as it can be used to represent several linguistic terms or comparative linguistic expressions together with some context-free grammars. This new approach has enabled the analysis and computing of linguistic expressions with uncertainties and opened the door for the possibility to develop more comprehensive and powerful decision theories and methods based on linguistic knowledge. Lots of new approaches and proposals for decision-making problems have been proposed to overcome the limitations of previous linguistic decision-making approaches. Now and in the future, decision-making methodologies and algorithms with hesitant fuzzy linguistic models would be a quite promising research line representing a high-quality breakthrough in this topic. To facilitate the study on HFLTS theory, this paper makes a state-of-the-art survey on HFLTSs based on the 134 selected papers from Web of Sciences published from January 2012 to October 2017. We justify the motivation, definitions, operations, comparison methods and measures of HFLTSs. We also summarize the different extensions of HFLTSs. The studies on multiple criteria decision making (MCDM) with HFLTSs in terms of aggregation operators and MCDM methods are clearly reviewed. We also conduce some overviews on decision making with hesitant fuzzy linguistic preference relations. The applications, research challenges and future directions are also given.

Hesitant Fuzzy Linguistic Term Set and Its Application in Decision Making: A State-of-the-Art Survey

An overview on feedback mechanisms with minimum adjustment or cost in consensus reaching in group decision making: Research paradigms and challenges

Due to the uncertainty of decision environment and differences of decision makers’ culture and knowledge background, multi-granular HFLTSs are usually elicited by decision makers in a multi-attribute group decision making (MAGDM) problem. In this paper, a novel consensus model is developed for MAGDM based on multi-granular HFLTSs. First, it is defined the group consensus measure based on the fuzzy envelope of multi-granular HFLTSs. Afterwards, an optimization model which aims to minimize the overall adjustment amount of decision makers’ preference is established. Based on the model, an iterative algorithm is devised to help decision makers reach consensus in MAGDM with multi-granular HFLTSs. Numerical results demonstrate the characteristics of the proposed consensus model.

Consensus reaching for MAGDM with multi-granular hesitant fuzzy linguistic term sets: a minimum adjustment-based approach

Confidence consensus-based model for large-scale group decision making: A novel approach to managing non-cooperative behaviors

Reaching a consensus with minimum adjustment in MAGDM with hesitant fuzzy linguistic term sets

In real decision making, due to the limitation of knowledge, problem complexity and time pressure, it is natural that decision makers express their opinions with incomplete linguistic information. This paper developed a novel consensus model for group decision making (GDM) with incomplete linguistic distribution assessments (ILDAs). Firstly, we defined the concept of ILDAs and the consensus measure for GDM with ILDAs. Then, we presented a consensus-oriented aggregation model, which can obtain a collective opinion with maximum consensus, and further developed a minimum-cost consensus model with variable unit consensus cost. Following this, based on the proposed aggregation and consensus models, we proposed a novel consensus reaching process for GDM with ILDAs. Finally, a numerical example is conducted to demonstrate the validity of the proposed consensus reaching process.

The optimization-based aggregation and consensus with minimum-cost in group decision making under incomplete linguistic distribution context

Minimum deviation ordinal consensus reaching in GDM with heterogeneous preference structures

Minimum information-loss transformations to support heterogeneous group decision making in a distributed linguistic context

In group decision making (GDM), consensus level is regarded as a critical criterion to measure the effectiveness and availability of the final group decision solution. Consensus model is aimed at conducting the decision group to reach agreement through the process of group negotiation, advice feedback, and opinion modification, which is time-consuming and rests with the willingness and behavior of individual decision makers. Thus, to guide the shift in the opinions of decision makers within a limited time, it is essential to design an effective, interpretable, and fair consensus mechanism in GDM, which is particularly vital when a mass of decision makers (e.g., more than 30) are involved in the decision process, viz., we encounter a large-scale GDM (LSGDM). With the involvement of information granulation, this study presents a rule-based consensus model in LSGDM by optimally allocating the level of information granularity to each decision maker. The opinions of decision makers in LSGDM are divided into different clusters by engaging the fuzzy $C$ -means method. Inspired by a generic fuzzy rule-based model, the radius of the individual preference granule (PG) is calculated by a weighted linear combination of the granularity levels allocated to the clusters. Then, a consensus model with the optimal allocation of information granularity (CMOIG) is built to determine the granularity level for each cluster by minimizing the sum of radii of individual PG. An interactive consensus reaching process is proposed with the proposed CMOIG and fuzzy modification rules. The CMOIG and fuzzy modification rules simultaneously guarantees high efficiency and interpretability, and the generation method of PGs leads to high fairness due to low discrepancy among the decision group. Finally, numerical and comparative experiments are conducted in detail to verify the validity and superiority of the presented models in terms of the efficiency, interpretability, and fairness.

Bowen Zhang

Papers

Reaching a consensus with minimum adjustment in MAGDM with hesitant fuzzy linguistic term sets

The optimization-based aggregation and consensus with minimum-cost in group decision making under incomplete linguistic distribution context

Minimum deviation ordinal consensus reaching in GDM with heterogeneous preference structures

Minimum information-loss transformations to support heterogeneous group decision making in a distributed linguistic context

Consensus Model Driven by Interpretable Rules in Large-Scale Group Decision Making With Optimal Allocation of Information Granularity