Group Decision-making Model Research Articles

A quantum cognition based group decision making model considering interference effects in consensus reaching process

Group decision making (GDM) is widely used in a complex and uncertain real-world to help multiple experts collectively evaluate and select appropriate alternatives. Consensus reaching process (CRP) is a critical step to help experts reach agreement in GDM. There exist interference effects in CRP since subjective beliefs of experts can drive interference effects. However, interference effects between experts are rarely considered. Superposition states in quantum can effectively explain the interference effects due to the superposition of expert beliefs. Therefore, this paper proposes a new quantum cognition-based group decision-making model considering the interference effects between experts during the CRP by integrating quantum probability theory. Firstly, a quantum Bayesian network is constructed to explore the interference effects of experts' psychology in GDM. Secondly, experts' weights are updated based on their contributions influenced by the inter-expert’s interference, and it can reflect experts' subjective attitudes. Then, only the individual preferences furthest from the group preferences are modified. The above two models are applied together in the feedback mechanism. Thirdly, the quantum Bayesian networks continuously prioritize alternatives based on modified experts' weights and preferences. The proposed model can better model the conflict, ambiguity, and uncertainty in GDM and perceive changes in psychological interference among experts during CRP. Finally, the proposed model is verified by applying a personnel selection example. Detailed sensitivity analysis and comparative analysis are presented to show the novelty and validity of the proposed model.

Enterprise Group Decision-making Model under Cloud Computing

Enterprise Group Decision-making Model under Cloud Computing

Mining Personalized Individual Semantics of Self-confidence Participants in Linguistic Group Decision-Making

Words representing individual preferences in group decision-making (GDM) are always associated with different meanings. Consequently, mining personalized semantics of decision-makers (DMs) hidden in preference expressions, and establishing a corresponding management mechanism, is an effective way to reach group consensus through computing with word methodology. However, the aforementioned consensus-reaching process may be hindered by self-confidence. To address this limitation, this study proposes a linguistic group decision model with self-confidence behavior. First, we identified the corresponding self-confidence levels for each DM. Next, we integrated different linguistic representation models into unified linguistic distribution-based models. We then obtained individual personalized semantics based on a consistency-driven optimization method, and designed a feedback-adjustment mechanism to improve the adjustment willingness of DMs and group consensus level. Finally, we conducted a quantitative experiment to demonstrate our model’s effectiveness and feasibility.

A consensus model for large scale group decision making with hesitant fuzzy linguistic information and hierarchical feedback mechanism

Large scale group decision making (LSGDM) has become a hot research topic in the field of decision science. Reaching a consensus in LSGDM is more difficult and complex than the traditional group decision making (GDM). In this paper, we propose a consensus model with hierarchical feedback mechanism for LSGDM dealing with hesitant fuzzy linguistic information. According to the majority rule, the sub-groups with more members are the ones playing a key role in the consensus reaching process. So the sub-groups are divided into three categories including dominant influencer, medium level of influencer and non-influencer. Then, to keep as much information as possible, we use possibility distributed extended hesitant fuzzy linguistic term sets to represent the sub-group opinion. After that, we take into account the sub-group size and the level of togetherness of the sub-group preferences to obtain the weight of each sub-group. Furthermore, a new hierarchical feedback mechanism is developed to provide different identification rules and recommendation strategies for the sub-groups performing different profiles. Finally, an application example of water resource management is utilized to show the effectiveness of the proposed model.

A Multidimensional Game Theory–Based Group Decision Model for Predictive Analytics

An N-dimensional game theory–based model for multi-actor predictive analytics is presented in this article. The proposed model expands our previous work on two-dimensional group decision model for predictive analytics. The one-dimensional models are used for the problems where actors are interacting in a single issue space only. This is less than an ideal assumption since; in most cases, players’ strategies may depend on the dynamics of multiple issues when dealing with other players. In this work, the one-dimensional model is expanded to N-dimensional model by considering different positions, and separate salience values, across different axes for the players. The model predicts an outcome for a given problem by taking into account stakeholder’s positions in different dimensions and their conflicting perspectives. To illustrate the capability of the proposed model, three case studies have been presented.

Multi-attribute group decision-making model for selecting the most suitable construction company using the linguistic interval-valued T-spherical fuzzy TOPSIS method

Multi-attribute group decision-making model for selecting the most suitable construction company using the linguistic interval-valued T-spherical fuzzy TOPSIS method

Linguistic Z-number preference relation for group decision making and its application in digital transformation assessment of SMEs

In the new phase of technological revolution and industrial transformation, the digital transformation (DT) of small and medium-sized enterprises (SMEs) has become a critical strategic issue. Assessing the DT of SMEs as a crucial link can be viewed as a group decision making (GDM) problem with uncertain linguistic information. Recently, the linguistic Z-number (LZN) has demonstrated benefits in the representation of uncertain linguistic information due to the characteristics of simultaneous representation of linguistic evaluation and corresponding credibility. However, rather of providing direct evaluation, decision makers (DMs) of the DT assessment prefer to pairwise compare alternatives. To this end, we put forward linguistic Z-number preference relations (LZPRs) as an assessment tool for expressing DMs’ preferences and credibilities. Following that, novel operational rules of LZNs are proposed to correspond with LZPRs. After considering the difference between the importance of the two parts A and B in LZNs, we further propose the weighted Euclidean distance and the improved comparison method of LZNs. In addition, the additive consistency of the LZPR is discussed, as well as a consistency index for determining its consistency. The automatic improvement algorithm and the feedback improvement method are used to enhance the consistency of LZPRs that have unacceptable consistency. Furthermore, a GDM model of LZPRs is built using DMs’ combined weights based on the credibility and the novel linguistic Z-number weighted averaging (NLZWA) operator. Finally, a case study and comparative analysis of SMEs’ DT assessment demonstrate the effectiveness, flexibility and superiority of the proposed method.

A large-scale group decision-making model with no consensus threshold based on social network analysis

Recently, large-scale group decision-making in the social network context has become an attractive research hotspot in decision science. The focus of large-scale group decision-making is how to objectively and reasonably generate a collective opinion accepted by most decision makers. To this end, this study investigates a novel consensus model with no threshold based on social networks. In the model, inspired by the “similarity-attraction paradigm” in psychology, we first propose a novel adjustment method for the trust degree between decision makers. A clustering method that depends on the similarity-trust score between decision makers is presented to reduce the complexity of decision-making. In the consensus reaching process, we provide an approach to managing the overconfident or unconfident behaviour of decision makers. Moreover, the social network DeGroot model is used to adjust the preferences of decision makers in the feedback mechanism. In addition, we design an objective condition to terminate the consensus reaching process. Based on this, the no threshold consensus model is established, which makes the decision results more rational and scientific. Finally, the applicability of the proposed model is demonstrated by an illustrative example. Simulation experiments and comparative analysis illustrate the validity of the proposed model in promoting consensus reaching.

RETRACTED: A method based on cloud model and FCM clustering for risky large group decision making

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.

Classical dynamic consensus and opinion dynamics models: A survey of recent trends and methodologies

Consensus reaching is an iterative and dynamic process that supports group decision-making models by guiding decision-makers towards modifying their opinions through a feedback mechanism. Many attempts have been recently devoted to the design of efficient consensus reaching processes, especially when the dynamism is dependent on time, which aims to deal with opinion dynamics models. The emergence of novel methodologies in this field has been accelerated over recent years. In this regard, the present work is concerned with a systematic review of classical dynamic consensus and opinion dynamics models. The most recent trends of both models are identified and the developed methodologies are described in detail. Challenges of each model and open problems are discussed and worthwhile directions for future research are given. Our findings denote that due to technological advancements, a majority of recent literature works are concerned with the large-scale group decision-making models, where the interactions of decision-makers are enabled via social networks. Managing the behavior of decision-makers and consensus reaching with the minimum adjustment cost under social network analysis have been the top priorities for researchers in the design of classical consensus and opinion dynamics models.

A fuzzy multicriteria group decision approach for classification of failure modes in a hospital’s operating room

The operating room (OR) is one of the most important departments of any hospital. Mismanagement can lead to severe problems with patient’s safety and satisfaction, costs, and profitability. The Failure Mode and Effects Analysis (FMEA) is a risk assessment tool used to identify potential failure modes (FM) which should be prioritized to be mitigated. To assess the risks of an OR, some important aspects cannot be neglected: it should be conducted by a team of experts from different functional areas; address the non-compensation between criteria; classify FM, instead of ranking it, since it is easier to understand and support decision making; address the subjectivity present on experts’ opinion; and consider the mitigation complexity, since hospital’s resources are limited. However, were not found studies in the literature which simultaneously covered all these aspects. Therefore, this paper proposes a new group decision model, which combines a consensus reaching process with ELECTRE TRI and double hierarchy hesitant fuzzy linguistic term sets (DHHFLTS). A pilot application of the model was conducted in an OR of a Brazilian hospital. The results have shown that the consensus technique, the mitigation complexity, and the non-compensation can modify the failure modes classification. Also, the DHHFLTS allowed the experts to use more complex linguistic terms to express their opinion. In addition, the pilot application was carried out during the covid-19 pandemic and the results have shown that due to the high number of elective surgeries being rescheduled to give space to urgent surgeries, the patients' health problems become even worst and they turn to urgent surgeries.

Integrated multi-criteria group decision-making model for supplier selection in an uncertain environment

Integrated multi-criteria group decision-making model for supplier selection in an uncertain environment

A Quantum Framework for Modeling Interference Effects in Linguistic Distribution Multiple Criteria Group Decision Making

In most existing linguistic distribution multiple criteria group decision-making (MCGDM) models, the sample size of group linguistic distribution assessment (LDA) is often neglected, and the decision makers are viewed to be independent. The probabilistic distribution is closely related to the sample size, and there exist more or fewer interference effects among different individual opinions. Thus, this article develops a quantum framework for modeling interference effects in the linguistic distribution MCGDM process. First, considering the sample size information, we redefine the LDAs and provide a new computational model for them. Second, to integrate a series of LDAs, the linguistic distribution weighted averaging (LDWA) operator is presented. Mathematic proofs reveal that the new LDWA operator can not only ensure the information integrality but also can avoid the conjunctive and disjunctive operations. Third, to explore the interference effects among individual opinions, a quantum framework is constructed. In this process, individual opinions are viewed as various wave functions occurring synchronously. They interfere with each and influence the aggregation result. Finally, an illustrative example of Internet finance soft power evaluation is provided to verify the effectiveness. Sensitivity and comparative analyses are also implemented to assess the stability and validity of our method.

A new three-way group decision-making model based on geometric heronian mean operators with q-rung orthopair uncertain linguistic information

As an effective tool for three-way decisions (3WD) problems, decision-theoretic rough sets (DTRSs) have raised increasing attention recently. In view of the advantages of q-rung orthopair uncertain linguistic variables (q-ROULVs) in depicting uncertain information, a new DTRSs model based on q-ROULVs is proposed to solve three-way group decision-making (3WGDM) problems. Firstly, the loss function of DTRSs is depicted by q-ROULVs and a q-rung orthopair uncertain linguistic DTRSs model is constructed subsequently. Secondly, to aggregate different experts’ evaluation results on loss function in group decision-making (GDM) scenario, the q-rung orthopair uncertain linguistic geometric Heronian mean (q-ROULGHM) operator and the q-rung orthopair uncertain linguistic weighted geometric Heronian mean (q-ROULWGHM) operator are presented. Related properties of the proposed operators are investigated. Thirdly, to compare the expected loss of each alternative, a new score function of q-ROULVs is defined and the corresponding decision rules for 3WGDM are deduced. Finally, an illustrative example of venture capital in high-tech projects is provided to verify the rationality and effectiveness of our method. The influence of different conditional probabilities and parameter values on decision results is comprehensively discussed.

Complex Pythagorean uncertain linguistic group decision-making model based on Heronian mean aggregation operator considering uncertainty, interaction and interrelationship

To effectively solve the mixed problem of considering the uncertainty of individuals and groups, the interaction between membership degree (MD) and non-membership (ND), and the interrelationship between attribute variables in complicated multiple attribute group decision-making (MAGDM) problems, in this paper, a concept of complex Pythagorean uncertain linguistic (CPUL) set (CPULS) is introduced, the interaction operational laws (IOLs) of CPUL variables (CPULVs) are defined. The CPUL interaction weighted averaging and geometric operators are presented. A new concept of CPUL rough number (CPULRN) is further constructed. The CPUL rough interaction weighted averaging and geometric aggregation operators (AOs) are extended. The ordering rules of any two CPULRNs are defined. The CPUL rough interaction Heronian mean (HM) (CPULRIHM) operator and its weighted form are advanced, related properties and special cases are explored. An MAGDM model based on CPUL rough interaction weighted HM (CPULRIWHM) operator is built. Lastly, we conduct a case study of location selection problem for logistics town project to show the applicability of the proposed methodology. The sensitivity and methods comparison are analyzed to verify the effectively and superiority.

A clustering- and maximum consensus-based model for social network large-scale group decision making with linguistic distribution

Nowadays, with the increasing complexity of decision-making environment, more and more large-scale group decision making (LGDM) problems are faced. Due to the existence of social network relationships among experts, social network analysis (SNA) is proved to be an effective analysis method for LGDM problems. Meanwhile, it is crucial for LGDM issues to determine the weights of decision groups and to lessen the large-scale DMs’ dimension, which will affect the result of decision making directly. This study proposes a clustering- and maximum consensus-based resolution framework with linguistic distribution (LD) for social network large-scale group decision making (SNLGDM) problems. In the consensus framework, independent sub-groups can be obtained by the division of large-scale DMs according to trust relationship using the proposed SNA-based trust network clustering model, and the LD assessments are used to represent the preference relation of sub-groups. Following this, by considering three dependable sources: consistency, similarity, and in-centrality degree, this paper devises a maximum consensus-based method, which can generate the sub-groups’ comprehensive weight by maximizing the level of consensus between sub-groups and the collective matrix. Meanwhile, the final ranking of alternatives can be obtained based on collective preference relation. Conclusively, the availability and advantage of this research are verified through numerical example, coefficient analysis and comparative analysis.

Visual social network group consensus method with improved PageRank algorithm

PurposeThe purpose of this study is to build a consensus model of social network group decision-making (SNGDM) based on improved PageRank algorithm. By objectively and fairly measuring the evaluation ability of participants in the decision-making process, the authors can improve the fairness and authenticity of the weight solution of decision-makers (DM) in the decision-making process. This ensures the reliability of the final group consensus results.Design/methodology/approachThis study mainly includes six parts: preference expression, calculation of DM's weight, preference aggregation, consensus measurement, opinion adjustment and alternative selection. First, Pythagorean fuzzy expression is introduced to express the preference of DMs, which expands the scope of preference expression of DMs. Second, based on the social network structure among DMs, the process of “mutual judgment” among DMs is increased to measure the evaluation ability of DMs. On this basis, the PageRank algorithm is improved to calculate the weight of DMs. This makes the process of reaching consensus more objective and fair. Third, in order to minimize the evaluation difference between groups and individuals, a preference aggregation model based on plant growth simulation algorithm (PGSA) is proposed to aggregate group preferences. Fourth, the consensus index of DMs is calculated from three levels to judge whether the consensus degree reaches the preset value. Fifth, considering the interaction of DMs in the social network, the evaluation value to achieve the required consensus degree is adjusted according to the DeGroot model to obtain the overall consensus. Finally, taking the group preference as the reference, the ranking of alternatives is determined by using the Pythagorean fuzzy score function.FindingsThis paper proposes a consensus model of SNGDM based on improved PageRank algorithm to aggregate expert preference information. A numerical case of product evaluation is introduced, and the feasibility and effectiveness of the model are explained through sensitivity analysis and comparative analysis. The results show that this method can solve the problem of reaching consensus in SNGDM.Originality/valueDifferent DMs may have different judgment criteria for the same decision-making problem, and the angle and depth of considering the problem will also be different. By increasing the process of mutual evaluation of DMs, the evaluation ability of each DM is judged only from the decision-making problem itself. In this way, the evaluation opinions recognized by most DMs will form the mainstream of opinions, and the influence of corresponding DMs will increase. Therefore, in order to improve the fairness and reliability of the consensus process, this study measures the real evaluation ability of DMs by increasing the “mutual judgment” process. On this basis, the defect of equal treatment of PageRank algorithm in calculating the weight of DMs is improved. This ensures the authenticity and objectivity of the weight of DMs. That is to improve the effectiveness of the whole evaluation mechanism. This method considers both the influence of DMs in the social network and their own evaluation level. The weight of DMs is calculated from two aspects: sociality and professionalism. It provides a new method and perspective for the calculation of DM’s weight in SNGDM.

A consensus model for large-scale group decision making based on empathetic network analysis and its application in strategical selection of COVID-19 vaccines

Effective COVID-19 vaccines are the best weapons against the COVID-19 pandemic and have become a strategic property of local governments. Experienced experts need to be invited to improve the accuracy of COVID-19 vaccine selection; such a selection process can be regarded as a large-scale group decision-making (LSGDM) problem. Many LSGDM models have been proposed in order to overcome the non-independence of experts. However, the objective empathetic relationships among experts which can affect decision results have been ignored. To fill these gaps, this article proposes a LSGDM method based on empathetic network analysis (ENA). First, we identify the dissociative empathetic network, central empathetic network, and general empathetic network. Then, we determine the results of internal preference evolution from the perspective of preference interactions. We adopt the fuzzy c-means (FCM) clustering algorithm to divide a large group of experts into several subgroups according to the empathetic centrality of experts, and then propose three kinds of feedback mechanisms with respect to the empathetic relationships for central, dissociative, and general empathetic networks to improve the quality of the consensus-reaching process. Finally, an illustrative example related to the selection of COVID-19 vaccines is presented to validate the proposed model.

A genetic algorithm based on dual hesitant fuzzy preference relations for consensus group decision making

Consensus is an important issue in group decision making as it aims to avoid future contestation by the decision makers. The elicitation of criteria and decision makers’ weights is an essential part of decision-making problems. This study proposes a new consensus model for group decision making based on dual hesitant fuzzy and evolutionary algorithm for the definition of unknown criteria and decision makers’ weights. The Dual Hesitant Fuzzy Preference Relations combines the advantages of intuitionistic and hesitant fuzzy representations, and it is used to deal with the imprecision in decision makers’ judgments. In order to find decision makers weights to reach a better level of consensus without the need to modify initial assessments, the proposed Genetic Algorithm (GA) is applied. An illustrative application case is presented in a large steel company considering sustainable criteria. An instance generator was proposed to create different scenarios of decision making varying the number of criteria, decision makers, and level of hesitation. Several instances were used in the computational tests for the evaluation of the GA performance. The effectiveness of the proposed GA was verified by comparing its results with the results of the implemented Particle Swarm Optimization (PSO) algorithm. The GA yielded solutions with improved consensus levels in a reasonable runtime, especially for a small or medium number of decision makers.

A fuzzy group decision-making model to measure resiliency in a food supply chain: A case study in Spain

The concept of supply chain management (SCM) has transformed and evolved beyond the simple rationality of benefit and economic viewpoints. With changing societal requirements in terms of the global risk-based economy, SCMs should be resilient and flexible to handle risk and quickly respond to disruptions. A food supply chain (FSC) is a critical global supply chain network due to its crucial role in meeting the growing consumer demand for edible products. Any disruptions and risks in FSC management (FSCM) may lead to irrecoverable and costly consequences; therefore, the resiliency of key players in the food SCM is very important. In this regard, this paper develops a decision-making model using the best worst method (BWM) and fuzzy measurement of alternatives and ranking according to compromise solution (fuzzy MARCOS) to measure the resiliency of key players in the FSCM with respect to different resiliency and risk factors. Sensitivity analysis tests to examine the reliability of the model are conducted. A case study of the agricultural FSCM of Andalusia Province of Spain is used to investigate the practicability of the proposed model. The results indicate that natural disasters and water system failures are two important risk factors with the highest weight coefficients, and excessive inventory was determined to be the least important risk factor. Moreover, supermarkets and wholesalers are determined to be the most resilient players in the FSCM of Andalusia Province of Spain.