A CONSENSUS MODEL FOR GROUP DECISION MAKING PROBLEMS WITH UNBALANCED FUZZY LINGUISTIC INFORMATION

Hesitant fuzzy linguistic term set HFLTS is a very useful technology in dealing with decision-making problems where people have hesitancy in providing their linguistic assessments. Distinct methods have been developed to aid decision making with HFLTSs, yet there is little research involving the issue that how to deal with the multigranularity hesitant fuzzy linguistic information. The aim of this paper is to develop the aggregation method for multigranularity hesitant fuzzy linguistic information and solve the linguistic group decision problem with different linguistic term sets. To do so, we first modify the translation functions and aggregation operators in the existing 2-tuple linguistic representation models so as to aggregate linguistic terms from different linguistic term sets. Then, we introduce the notion of hesitant 2-tuple sets to make computation of HFLTSs without loss of information, and develop some new operators to aggregate HFLTSs from different linguistic term sets. Using these operators, we propose a method to deal with multigranularity linguistic group decision-making problems with different situations where importance weights of either criteria or experts are known or unknown. Finally, the multigranularity linguistic group decision-making model is implemented to the healthcare waste treatment in West China Hospital to validate its effectiveness and efficiency in aiding decision-making process.

The preference values in group decision-making (GDM) process can differ significantly between different experts, which may yield a low level of group consensus. Therefore, different consensus models have been developed for the modification of preference values to assist experts in improving their consensus degrees. However, most consensus models do not consider collective intelligence (CI) that may decrease as the consensus degree increases under certain circumstances. From the perspective of CI, the distrust relationship allows the group to better explore the decision space, rather than prematurely converge on an agreed suboptimal solution. Inspired by this idea, a theoretical framework of solving intuitionistic fuzzy GDM problems with low group consensus is proposed in this paper, which mainly includes two steps: (1) building the trust/distrust relationships and (2) establishing a consensus model. For two experts with a direct relationship, the trust/distrust relationships between them are constructed by fusing their knowledge levels and representativeness levels. For two experts with an indirect relationship, a new operator is designed to construct the trust/distrust relationships between them, which can describe the information attenuation of the decreasing trust along with the increasing distrust. Additionally, a consensus model based on the social network relationships density and trust/distrust relationships is proposed, which improves consensus degree and CI level conducively. Finally, a ranking of alternatives is constructed to select the optimal alternative. An illustrative example is used to demonstrate the effectiveness and applicability of the proposed method.

2-tuple fuzzy linguistic model can be applied to eliminate vagueness/uncertainty in information. It also helps to deal with non-homogeneous information that occurs during group decision making (GDM) processes. GDM is generally applied to diminish the bias during the decision phase and to reduce the subjectivity of the decision process. Thus, this paper proposes an integrated GDM technique based on 2-tuple linguistic model, quality function deployment (QFD) and the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method. This proposed framework is then applied to a green warehouse selection problem. The originality of this paper comes from its combination of these two methodologies together for the first time in literature in this specific field. The application area indicates its suitability for the proposed methodology.

Different consensus models for group decision-making (GDM) problems have been proposed in the literature. However, all of them consider the consensus reaching process a rigid or inflexible one because its behavior remains fixed in all rounds of the consensus process. The aim of this paper is to improve the consensus reaching process in GDM problems defined in multigranular linguistic contexts, i.e., by using linguistic term sets with different cardinality to represent experts' preferences. To do that, we propose an adaptive consensus support system model for this type of decision-making problem, i.e., a process that adapts its behavior to the agreement achieved in each round. This adaptive model increases the convergence toward the consensus and, therefore, reduces the number of rounds to reach it.

Compatibility measures and consensus models for group decision making with intuitionistic multiplicative preference relations

An Analysis of Several Novel Frameworks and Models in the Consensus Reaching Process

Personalized individual semantics in computing with words for supporting linguistic group decision making. An application on consensus reaching

When qualitative data are collected, which is a widespread situation encountered in several disciplines ranging from social sciences to decision making to engineering as a result of dealing with ill-defined concepts or with complex tasks to assess, there is a certain need to crunch them and infer from them. Hence, quantification, namely computing with words (CW), emerges as a research area. Even though social science disciplines deal with these types of data numerically using various Likert scales, fuzzy set theory first proposed by Zadeh dealing words or sentences with mathematically oriented manner in order to create machines that mimic the reasoning of human being brings new perspectives. Since then, its applicability has expanded into various disciplines for different purposes to transform subjectivity into objectivity. In this manuscript, the effort of transformation from subjectivity into objectivity will be reviewed based on the available proposed methods in the literature. Two illustrative examples will be employed. While the first illustrative example, which consists of balanced and unbalanced linguistic term sets, is used for each reviewed method in the literature to show its computations step by step, the second of which, balanced linguistic term set, is an example used in the literature that is employed. Therefore, a comprehensive review of the proposed methods with illustrative examples is presented.

Unconventional outburst incidents are unpredictable and also have no emergency plans, they can bring huge economic losses and social issues, so we should find optimal emergency decision scheme. In this paper, we study the group decision making (GDM) problems with incomplete fuzzy linguistic preference relations (FLPRs), and the linguistic information provided by different experts are assessed in linguistic term sets with different granularity. We first develop a four-way procedure to estimate missing preference values when dealing with 2-tuple incomplete FLPRs. We propose the transformation rules for multi-granular FLPRs, and devise a transformation function which satisfies the transformation rules. The transformation can transform any linguistic term set to another linguistic term set. Furthermore, we study the properties and advantages of the transformation function. Finally, we integrate the revised estimation procedure for incomplete 2-tuple FLPRs and the multi-granular linguistic decision model to deal with emergency decision of unconventional outburst incidents. It is showed that the proposed method is straightforward and without loss of information.

Group decision-making (GDM) is a process that consists of choosing the best alternative or a set of alternatives from all possible, taking into account the opinions of the group of people. This technology is well researched and applied not for the first decade, however, traditional algorithms did not intend to work with a large amount of data. It means, that during assessment of alternatives each expert chooses a limited set of data that interested him. In addition, each of experts may have his own rating scale. The problem becomes critical when the number of alternatives and experts evaluating them is large, as it is happening in social network contexts. The article offers a review and a formal description of a new GDM approach in social networks. With the use of set-theoretical operations, method of alternatives ranking in the group assessment of social networks is formalized. A theorem on the conversion from the rating scale of linguistic term set (LTS) into basic LTS (BLTS) rating scale is proved. Using the UML language, a formal model was developed and a key algorithm for conversion of numerical ranks from the LTS scale into the BLTS scale was proposed. A method for extrapolating the values of ranks when the network is scaled is developed, for example, when the number of experts is modified. A case for numerical demonstration of the algorithm is presented.

A consensus model for group decision-making with personalized individual self-confidence and trust semantics: A perspective on dynamic social network interactions

In practice, when the frequency of a linguistic term in evaluation information is greater than one, a consensus has been reached and this frequency is regarded to be one by default. However, in case that the frequencies of several elements in a dataset are not identical, their respective importance that was initially distinguishable will become indifferent in a routine processing, which may distort the original intention of decision-makers. As an extension of hesitant fuzzy linguistic term sets (HFLTSs) and hesitant fuzzy linguistic sets (HFLSs), multi-hesitant fuzzy linguistic term sets (MHFLTSs) are thus proposed on the basis of multi-hesitant fuzzy sets (MHFSs) in this paper, and each multi-hesitant fuzzy linguistic term element (MHFLTE) can contain inconsecutive and repeatable linguistic terms. Besides, inspired by the 2-tuple linguistic model and linguistic scale functions, the transformation functions among linguistic terms, 2-tuples, and numerical data are constructed in a generalized form to cope with different semantics. According to practical conditions, decision-makers’ preferences, or experts’ suggestions, various functions can be chosen after some necessary adjustments. In this way, the original essence of vague evaluations can be retained properly and the accuracy and reliability of final results could be further improved. Furthermore, the basic operations and comparison method of MHFLTEs are given on the basis of the transformation functions, and some 2-tuple linguistic aggregation operators of MHFLTEs are also provided to deal with problems where the criteria weights are known or unknown. Based on these prerequisites, a multi-criteria group decision-making approach is established to process multi-hesitant fuzzy linguistic information. The validity and effectiveness of the proposed approach are finally demonstrated through illustration and comparison analysis.

A self-confidence and leadership based feedback mechanism for consensus of group decision making with probabilistic linguistic preference relation

Consistency and consensus reaching process for group decision making based on complete interval distributed preference relations under social network analysis

A dynamic dual-trust network-based consensus model for individual non-cooperative behaviour management in group decision-making