Group Decision-making Problems Research Articles

Spherical Fuzzy Choquet Integral-Based VIKOR Method for Multi-Criteria Group Decision-Making Problems

Inter-dependency among the decision criteria and difficulty of handling the information in form of “yes,” “abstain,” “no,” and “refusal” are two important issues to be addressed in multicriteria group decision-making (MCGDM) problems in the environment of uncertainty. Recently, spherical fuzzy set (SFS) has gained the attention of the researchers for MCGDM problems due to their capability of handling decision makers’ preferences in the form “yes,” “abstain,” “no,” and “refusal” more efficiently than picture fuzzy set. Choquet integral operator has an edge over traditional aggregation operator in the modeling of interaction among the preferences of decision makers and decision criteria and therefore, we define Choquet integral operator for SFS. The main objective of this study is to define the spherical fuzzy Choquet integral (SFCI) operator and to extend the VIsekriterijumska optimizacija I Kmpromisno Resenje (VIKOR) for spherical fuzzy environment. In this study we define spherical fuzzy Choquet average, spherical fuzzy Choquet geometric and spherical fuzzy Choquet integral distance (SFCID) operators and propose SFCI-based VIKOR method for MCGDM problems. Proposed SFCI-based VIKOR method is applied on warehouse selection problems and results are compared with existing MCGDM methods.

Time uncertainty and random opinion based group decision making for demolition negotiations

Current research on group decision making (GDM) focuses on how to quickly reach a consensus, in which decision makers’ opinions are mainly changed by feedback suggestion, not by changing circumstances over time. In a long-term GDM problem, decision-makers may be affected by the environment to change their views. For example, real estate market prices will affect residents’ opinions in time-consuming demolition negotiations. We introduce time uncertainty to study the long-term GDM problem with random opinions following this idea. This article constructs the minimum cost and maximum utility-based consensus models in GDM with random opinion based on the time uncertainty. The optimal time of decision-making can be determined. Specifically, the real options theory is employed to study time uncertainty. Furthermore, the feasibility of the models is verified by case analysis of China’s demolition negotiation for old communities’ renewal projects, and sensitivity analyses are developed to analyse the robustness of our models. In the case analysis and sensitivity analysis, we obtain the optimal solution of models by using a genetic algorithm based on MATLABR2018b.

A PCA-based fuzzy tensor evaluation model for multiple-criteria group decision making

Multiple-criteria group decision-making (MCGDM) problems mainly consist of multiple factors and multiple Decision Makers (DMs) or Users, for which dimension extension is necessary when considering all the entries of DMs together. Tensor, a generalized form of a matrix, displays a multi-way array item, which is the most suitable and practical way to represent high-dimensional data without losing any information. In this paper, we first reduce the dimension through Principal Component Analysis (PCA), which helps consider the most-informative criteria. Then, we reintroduced the tensor as a fuzzy-form tensor for the MCGDM problem because of user information uncertainty. We choose Interval-valued Neutrosophic Fuzzy numbers (IVNFNs) as the basis for the tensor form because of their ability to distinguish between truth, indeterminacy, and falsity in the data. Lastly, a Generalized Interval-valued Neutrosophic Fuzzy Weighted Geometric (GIVNFWG) operator is defined. Moreover, a generalized framework for fuzzy-form tensors for high-dimensional MCGDM problems is proposed. The feasibility and efficiency of this proposed process is illustrated for a real-world MCGDM problem of ranking the most efficient Third Party Reverse Logistics Partners (3PRLPs), i.e., recycled fiber-based paper mills for the packaging industry. The obtained results are according to the experts and are validated using sensitivity analysis. This analysis facilitates in assessing the impact on the overall ranking performance of 3PRLPs by considering various combinations of environment and technological sub-criteria.

Group decision making with hesitant fuzzy linguistic preference relations based on multiplicative DEA cross-efficiency and stochastic acceptability analysis

The aim of this paper is to investigate a novel approach to group decision making (GDM) based on multiplicative DEA cross-efficiency and stochastic acceptability analysis with hesitant fuzzy linguistic preference relations (HFLPRs), which can avoid information distortion and obtain more credible decision-making results. First, a transform function is defined, which can extract effective information of the hesitant fuzzy linguistic term set (HFLTS) sufficiently. Then, an optimization model is proposed to derive the occurring probabilities of the elements in the HFLTS. It can avoid the normalization process of HFLPRs and simulate the element-selection process of decision makers (DMs). Moreover, a Maximum Log DEA Cross Efficiency model is proposed to evaluate the relative efficiency of each alternative from HFLPR. We further develop the stochastic weight space acceptability analysis method to solve the GDM problem and a step-by-step procedure is presented. Finally, numerical examples are given to illustrate the validity and applicability of the proposed method. This is the first attempt of employing the multiplicative DEA cross-efficiency to the GDM with HFLPRs.

Blockchain knowledge selection under the trapezoidal fermatean fuzzy number.

Blockchain knowledge signifies a useful fundamental knowledge to safeguard faith in transboundary transmittals for main banks and financial institutions. In the study of group decision-making, the most important issue is how to coordinate opinions from different blockchains to reach a compromise under uncertainty. To tackle uncertainties surrounding multi-attribute group decision-making (MAGDM) problems in real-life scenes, we introduce a trapezoidal fermatean fuzzy set which generalizes trapezoidal fuzzy sets and fermatean fuzzy sets. The trapezoidal fermatean fuzzy model enables the degrees of membership, abstention, and non-membership to be expressed by linguistic terms. We define the operational laws of trapezoidal fermatean fuzzy numbers, and Einstein aggregation operator based on the trapezoidal fermatean fuzzy number. This makes it more flexible and descriptive to model the attitudes of Blockchain knowledge in MAGDM applications. Since multi-input arguments are interconnected and Blockchain knowledge has a lot of options perception, we also define the TOPSIS technique to facilitate the fusion of trapezoidal fermatean fuzzy information. With the aid of the trapezoidal fermatean fuzzy-TOPSIS technique, the main goal of this research is to present a general MAGDM framework by integrating the step with the complex proportional assessment. A trapezoidal fermatean positive ideal solution always wants the maximum value of the benefit criteria and the minimum value of the cost criteria. On the other hand, the trapezoidal fermatean negative ideal solution always wants the maximum value of the cost criteria and the minimum value of the benefit criteria. An integrated trapezoidal fermatean fuzzy-TOPSIS framework is established. In the proposed decision framework, the trapezoidal fermatean fuzzy-TOPSIS method is utilized to identify the subjective weights of decision attributes, and the trapezoidal fermatean fuzzy-TOPSIS approach is used to rank alternatives. Lastly, a case study concerning blockchain knowledge assessment is presented to demonstrate that the suggested scheme is feasible and effective. Furthermore, sensitivity and comparison analyses are conducted to show the robustness and superiority of the proposed method.

An integrated methodology for utilization efficiency evaluation of college stadiums based on fuzzy number intuitionistic fuzzy multiple attribute group decision-making

In recent years, with the steady development of the national economy and the continuous improvement of people’s living standards, the desire for material pursuits has gradually transformed into the pursuit of spiritual food, and the attention to health and body is highly valued. It gave birth to and promoted the development of the sports industry. High-standard college stadiums provide many conveniences for students and faculty, and the construction and management of college stadiums are also an important part of the development of my country’s sports industry. However, there are still some drawbacks in the management mode and utilization efficiency of college stadiums. The utilization efficiency evaluation of college stadiums is frequently looked as the multiple attribute group decision-making (MAGDM) problem. Depending on the VIKOR process and fuzzy number intuitionistic fuzzy sets (FNIFSs), this paper designs a novel FNIF-VIKOR process to assess the resource utilization efficiency of college stadiums. First of all, some basic theories related to FNIFSs are briefly introduced. In addition, the weights of attributes are obtained objectively by utilizing CRITIC weight method. Afterwards, the conventional VIKOR process is extended to FNIFSs to obtain the final order of the alternative. Eventually, an application case for utilization efficiency evaluation of college stadiums and some comparative analysis are fully given. The results show that the built algorithms method is useful for assessing the resource utilization efficiency of college stadiums.

LEADERSHIP STYLE AND VARIABLES OF INFLUENCE ON GROUP DECISION-MAKING IN COMPANIES

Eurofound data shows that only 29% of employees in the European Union, Norway and the United Kingdom participate in the work of their organizations at a high level of involvement: they participate in making decisions that have an impact on the wider organization, individually or collectively, they show personal initiative in carrying out daily tasks. At a time when many companies are facing serious crises (inflation, problems in the functioning of supply chains, labor migration, threats of energy crises and many others), the question of how to make companies as flexible as possible to challenges, whether external or internal, arises as a necessity. This issue often requires the cooperation of experts from different areas of the company to solve complex problems. Likert’s participative leadership style encourages employee participation in group decision-making and joint problem solving. Encouraging employee engagement is a major challenge for organizations. This implies shaping a specific organizational culture that is participative, an organizational climate and a managerial style of behavior that is also participative. For this purpose, a questionnaire was created on the basis of which a sample of 125 respondents in companies in the Republic of Croatia was collected (KMO=.892, p.05, α=0.894). The sample includes companies of different sizes, as well as respondents of different educational levels, age and gender. The attitudes and perceptions of employees about group decision-making in companies, teamwork, communication, motivation, goals, supervision and influence of employees were examined. Answers to two research questions were obtained and two starting hypotheses were confirmed. The results of the processing of hypothesis H1 (ρ=.572, n= 125, p.05) suggest that a statistically significant connection was established between the variable “we decide on goals as a group” and the variable “my manager asks for my ideas and asks for my consent”. Results processing of hypothesis H2 (ρ=.685, n=164, p.05) suggest that a significant connection was established between the variable “we decide on goals as a group” and the variable “my company encourages teamwork and cooperation”. The regression analysis obtained predictors variables that have an impact on group decision-making in companies The proposed model, which statistically significantly predicts 51.1% of the criterion variance (R2 =.527, R adj=.511), defines four predictor variables (freedom to decide within the scope of one’s powers, my company encourages cooperation and teamwork, I have influence on my manager, we regularly analyze the achievement of goals) which have a statistically significant influence on the criterion variable (“we decide on goals as a group”). These findings have practical implications for menad concerns in companies when shaping a participative organizational culture and shaping a participative leadership style.

A multistage decision-making method with quantum-guided expert state transition based on normal cloud models

Multistage group decision problems are characterized by a changeable decision-making environment and a complex group consensus process, which make it difficult to obtain decision-making results. However, few methods have considered the phenomenon of expert state transition between manageable subgroups in multistage problems. In this paper, we propose a multistage decision-making framework considering quantum-guided expert transition based on normal cloud models. First, a minimum cost consensus model considering individual compromise intervals and the cost function is established based on the normal cloud models to obtain consensual subgroup opinions. Second, an attribute weight optimization model is established based on adjustment satisfaction and fairness satisfaction to aggregate the subgroups. Third, we define the satisfaction determination method of experts in the transition process and then apply the quantum framework to update their state in the next stage. Finally, the feasibility and applicability of the proposed method are illustrated via a power grid emergency evaluation example by conducting analyses and comparisons of the decision results.

T-spherical uncertain linguistic MARCOS method based on generalized distance and Heronian mean for multi-attribute group decision-making with unknown weight information

The T-spherical uncertain linguistic (TSUL) sets (TSULSs) integrated by T-spherical fuzzy sets and uncertain linguistic variables are introduced in this article. This new concept is not only a generalized form but also can integrate decision-makers’ quantitative evaluation ideas and qualitative evaluation information. The TSULSs serve as a reliable and comprehensive tool for describing complex and uncertain decision information. This paper focuses on an extended MARCOS (Measurement of Alternatives and Ranking according to the Compromise Solution) method to handle the TSUL multi-attribute group decision-making problems where the weight information is completely unknown. First, we define, respectively, the operation rules and generalized distance measure of T-spherical uncertain linguistic numbers (TSULNs). Then, we develop two kinds of aggregation operators of TSULNs, one kind of operator with independent attributes is T-spherical uncertain linguistic weighted averaging and geometric (TSULWA and TSULWG) operators, and the other is T-spherical uncertain linguistic Heronian mean aggregation operators (TSULHM and TSULWHM) considering attributes interrelationship. Their related properties are discussed and a series of reduced forms are presented. Subsequently, a new TSUL-MARCOS-based multi-attribute group decision-making model combining the proposed aggregation operators and generalized distance is constructed. Finally, a real case of investment decision for a community group-buying platform is presented for illustration. We further test the rationality and superiorities of the proposed method through sensitivity analysis and comparative study.

A new algorithm for computing priority vector of pairwise comparisons matrix with fuzzy elements

Applying the Analytic Hierarchy Process (AHP) in a decision making (DM) problem, fuzzy elements are appropriate whenever the decision maker is uncertain about the value of his/her evaluation of the relative importance of the elements in question, i.e., criteria and/or alternatives. The method, often called the fuzzy AHP, is also used when aggregating crisp pairwise comparisons of a group of decision makers in the group DM problem. In this paper, the DM problem is formulated in a general setting using pairwise comparisons matrices with elements from an Abelian linearly ordered group (alo-group). Such an approach enables extensions of traditional multiplicative, additive, or fuzzy approaches. Here, we propose some desirable properties (consistency, coherency, and intensity) of priority vectors, and derive sufficient conditions for the existence of priority vectors with those properties. In general, the most popular methods for deriving the priority vector – the Eigenvector Method and the Geometric Mean Method – do not always provide priority vectors having these desirable properties. Here, we formulate a new solution algorithm for deriving the priority vector based on a specific optimization problem satisfying the desirable properties under appropriate assumptions. Two illustrating examples of the algorithm are presented and discussed.

Dombi Aggregation Operators of Linguistic Neutrosophic Numbers for Multiple Attribute Group Decision-Making Problems in Landslide Treatment Schemes

Dombi Aggregation Operators of Linguistic Neutrosophic Numbers for Multiple Attribute Group Decision-Making Problems in Landslide Treatment Schemes

N-Cubic q-Rung Orthopair Fuzzy Sets: Analysis of the Use of Mobile App in the Education Sector.

This study analyzes the description to examine the results of a new study and create the technique and also demonstrate the effectiveness of this technique. In this ever-changing world, students are increasingly encouraged to use mobile phones primarily to learn for educational purposes. The learning process is continuous and the goal has now been achieved. It has been replaced by online learning. Due to mobile phones as well as the many feature-oriented applications, students can study at their own place and use the application to spend their time understanding, because everything is accessible with a single click. To carry on the study we applied mobile applications for online education system. Now, because the traditional method is taken into consideration, it is normal to carry a bag full of books and copies and immerse yourself in the tradition of learning to write. However, it has been found that not all students learn when he takes notes. Therefore, we must make sure that the student focuses only on one thing at a time. To continue the research, we apply the N-cubic structure to q-rung orthopair fuzzy sets in multi-attribute group decision-making problems. This structure solves the problems of multi-attribute group decision-making techniques more generally.

Introducing MOWSCER Method for Multiple Criteria Group Decision-Making: A New Method of Weighting in the Structure of Cause and Effect Relationships

One of the most remarkable subjects in multi-criteria group decision-making (MCGDM) is determining the weight and importance of criteria. The weighting methods based on inputs are categorized in the manifold group. This paper presents a novel method for weighting the criteria in a network structure. This approach, namely MOWSCER, is used when the relationships among the criteria are modeled by a cause-and-effect directed graph. The directed graph demonstrates the cause and effect relationship among criteria. In the presented method, the criteria are divided into three groups. The basic idea of the criteria segmentation is derived from European Foundation for Quality Management (EFQM). These groups include (1) effect criteria, (2) cause criteria, and (3) connector criteria. Then, the connector criteria are allocated fewer weights than the other two types. In other words, the introduced method follows two purposes of decreasing criteria number and appropriate allocating of weights among remaining criteria. Accordingly, first, the connector criteria are detected, so they are assigned less weight, and remained criteria are allocated a proper weight according to their importance. Furthermore, a new weighting method for determining the weights of decision makers (DMs) in group decision-making problems is presented to achieve a comprehensive manner. In the end, to prove the practicality of the proposed method, the weights of criteria and DMs are computed in a case study and two illustrative examples. Besides, to confirm the accuracy of that, it is compared with the DEMATEL method.

A two-stage multi-attribute group consensus model based on distributed linguistic assessment information from the perspective of fairness concern

The consensus reaching process (CRP) is a key step in group decision-making (GDM), in which reaching a satisfactory consensus often requires certain costs, such as time, money, and effort. Moreover, benefits from the CRP are often compared among decision-makers, which raises fairness issues. Given these, this paper comprehensively considers these two factors in linguistic GDM modeling. Aiming at the problem of multi-attribute group decision-making (MAGDM) under distributed linguistic assessment (DLA) information, a two-stage group consensus model that considers psychological behaviors with fairness concern is proposed. Subsequently, the feasibility and effectiveness of the proposed model are illustrated by a case of selecting emergency material warehouses related to urban flood disasters. The main innovations and contributions of this paper are as follows. (a) The psychology of individual fairness concern is integrated into the MAGDM process with DLA information. (b) The DLA-based two-stage group consensus model, including the first-stage consensus model with maximum fairness satisfaction degree and the second-stage consensus model with minimum cost, is developed. Compared with the existing consensus models for distributed linguistic MAGDM, the proposed consensus model can not only improve the consensus level on the opinions with minimum cost but also promote the fairness satisfaction degree of decision-makers.

Sustainable supply chain partner selection and order allocation: A hybrid fuzzy PL-TODIM based MCGDM approach

Sustainability, as a trend of social development and the embodiment of corporate social responsibility, has begun to receive more attention. To achieve this goal, sustainable supplier selection (SSS) and order allocation (OA) are seen as the crucial activities in corporate management. In the process of SSS, the psychological behavior of decision-makers (DMs) could play a critical role in the evaluation results. Therefore, introducing it into the decision-making process may lead to decision in line with the actual situation. In the uncertain multi-criteria group decision-making (MCGDM) problem described by probability linguistic term sets (PLTS), the DMs can evaluate the criteria of each supplier based on his own preference and hesitation, which is useful to avoid the loss of information. For this reason, this study develops a novel multi-criteria group decision-making combined with fuzzy multi-objective optimization (MCGDM-FMOO) model for SSS/OA problems by considering the triple bottom line (TBL) in which includes economic, environmental and social factors. The proposed method includes four stages. (1) the best-worst method (BWM) and entropy weight method are utilized to assign the weights of criteria to obtain the comprehensive weight. According to the output weights, the an acronym for interactive and multi-criteria decision-making in Portugese (TODIM) approach is applied to rank the suppliers under PLTS environment; (2) a FMOO model that can effectively deal with uncertainties and dynamic nature of parameter is formulated for allocating optimal order quantities; (3) two novel approaches are utilized to solve the FMOO model in order to obtain the richer Pareto frontier; and (4) the final OA solution is achieved by technique for order preference by similarity to ideal solution (TOPSIS) method. Finally, the validity and practicability of proposed MCGDM-FMOO model are verified by an example and comparative analysis with other classical MCGDM methods.

A novel two-stage TOPSIS approach based on interval-valued probabilistic linguistic q-rung orthopair fuzzy sets with its application to MAGDM problems

Fuzzy theories are widely used in multi-attribute group decision-making (MAGDM) problems to describe uncertain and hesitant information. The recently proposed probabilistic linguistic q-rung orthopair fuzzy set (PLq-ROFS) is capable in dealing with quantitative and qualitative information simultaneously. However, in many actual situations, decision-makers (DMs) prefer to utilize interval values to express their minds and evaluations, this paper employs interval values to represent the probabilistic distribution of the membership degrees (MDs) and non-membership degrees (NMDs), and proposes the interval-valued PLq-ROFS (IVPLq-ROFS). Based on which, a novel two-stage TOPSIS approach is introduced. The IVPLq-ROFS weighted extended power average (IVPLq-ROFWEPA) operator is proposed to obtain the comprehensive matrix and weights of each attribute, while a further advanced TOPSIS model is constructed to get the final rank of alternatives. An example of a new-type smart city development evaluation problem is given to illustrate the efficacy of the proposed approach. Results show that our approach is more flexible, more adaptable, more accurate, more freedom, and has much lower calculation complexity in the calculation process of the MAGDM problem. The contributions of the proposed method are mainly manifested in giving the concept of IVPLq-ROFSs, and proposing a novel two-stage TOPSIS model for dealing with MAGDM problems under IVPLq-ROFSs.

A novel consensus reaching method for MCGDM in social network with intuitionistic grey linguistic numbers

PurposeThe existing consensus reaching mechanisms ignore the influence of social triangle structure on the decision-makers’ (DMs') weights, and the consensus reaching process (CRP) fails to fully reflect the DMs' subjectivity and can be time consuming and costly. To solve these issues, a novel CRP for multi-criteria group decision-making (MCGDM) problems with intuitionistic grey linguistic numbers (IGLNs) is proposed in this paper.Design/methodology/approachFirst, a weight calculation method is proposed by analysing the triangle structure of DMs' social network and scale of adjacent nodes. Then, a consensus degree index based on three-level polygon area is defined and applied to identify the inconsistent DMs. Finally, the feedback mechanism based on particle swarm optimisation (PSO) algorithm under grey linguistic environment is developed, where subjective trust relationships in social network is utilised to determine the adjustment coefficient.FindingsThe advantages of the proposed method are highlighted by two practical applications of the evaluation of tunnel construction method and the selection of a hotel for the centralised isolation. Comparision analysis and numerical simulation are performed to reveal the effectiveness and applicability of the method.Practical implicationsThe proposed model can not only reflect the effect of triangle structure in social network on DMs' weights, but also reduce the time and cost of decision-making.Originality/valueThe main contribution of this paper is to propose a new MCGDM model based on intuitionistic grey linguistic numbers, which can handle the problem of inconsistency of information more effectively.

Learning personalized individual semantics through the data of distributed linguistic preference relations: A two-stage method to support linguistic consensus reaching

Distribution linguistic preference relation (DLPR) is an effective tool to model linguistic preferences with multiple linguistic terms, and has widespread adoption in the linguistic group decision making (GDM), in which we commonly accept the existence of personalized individual semantics (PISs). Currently, the existing research on PISs is driven by a consistency-based method and assumes the fixed PISs for decision makers. However, decision makers may update their PISs during the consensus reaching process (CRP), which implies the importance of learning PIS from the preference data. To analyze the changing PISs in the CRP in a linguistic GDM problem with DLPRs, we put forward a PIS-learning-based two-stage consensus approach by considering all data of DLPRs given by decision makers. The PIS-learning-based two-stage consensus approach uses the consistency-driven and consensus-driven methodology to update the personalized numerical scales (PNSs), and guarantee the acceptable consistency and consensus simultaneously. Finally, we apply the proposed method to the financial technology selection. Moreover, a comparative analysis verifies the effectiveness of the PIS-learning-based two-stage consensus approach for supporting GDM with DLPRs.

Relationship between the distance consensus and the consensus degree in comprehensive minimum cost consensus models: A polytope-based analysis

Agreement in Group Decision-Making problems has recently been tackled through the use of Minimum Cost Consensus (MCC) models, which are associated with solving convex optimization problems. Such models minimize the cost of changing experts’ preferences towards reaching a mutual consensus, and establish that the distance between the modified individual preferences and the collective opinion must be bounded by the threshold ε>0. A recent MCC-based model, called the Comprehensive Minimum Cost Consensus (CMCC) model, adds another constraint related to a parameter γ∈[0,1] to the above constraint related to the parameter ε to enforce modified expert preferences in order to achieve a minimum level of agreement dictated by the consensus threshold 1−γ∈[0,1]. This paper attempts to analyze the relationship between the aforementioned constraints in the CMCC models from two different perspectives. The first is based on inequalities and allows simple bounds to be determined to relate the parameters ε and γ. The second one is based on Convex Polytope Theory and provides algorithms that compute more precise bounds to relate these parameters, and could also be applied to other similar optimization problems. Finally, several examples are provided to illustrate the proposal.

A dynamic trust consensus model based on personalized adjustment mechanism under multi-granular probabilistic linguistic environment

PurposeThis study aims to propose a consensus model that considers dynamic trust and the hesitation degree of the expert's evaluation, and the model can provide personalized adjustment advice to inconsistent experts.Design/methodology/approachThe trust degree between experts will be affected by the decision-making environment or the behavior of other experts. Therefore, based on the psychological “similarity-attraction paradigm”, an adjustment method for the trust degree between experts is proposed. In addition, we proposed a method to measure the hesitation degree of the expert's evaluation under the multi-granular probabilistic linguistic environment. Based on the hesitation degree of evaluation and trust degree, a method for determining the importance degree of experts is proposed. In the feedback mechanism, we presented a personalized adjustment mechanism that can provide the personalized adjustment advice for inconsistent experts. The personalized adjustment advice is accepted readily by inconsistent experts and ensures that the collective consensus degree will increase after the adjustment.FindingsThe results show that the consensus model in this paper can solve the social network group decision-making problem, in which the trust degree among experts is dynamic changing. An illustrative example demonstrates the feasibility of the proposed model in this paper. Simulation experiments have confirmed the effectiveness of the model in promoting consensus.Originality/valueThe authors presented a novel dynamic trust consensus model based on the expert's hesitation degree and a personalized adjustment mechanism under the multi-granular probabilistic linguistic environment. The model can solve a variety of social network group decision-making problems.