Group Decision-making Research Articles

Multi-stage type-α constrained minimum-cost consensus for linguistic Z-number large-scale decision-making

As an extension of group decision-making in terms of scale and uncertainty, linguistic Z-number large-scale decision-making (LZ-LSDM) is emerging as a prominent research topic in the field of decision science. The unique structure of LZ-LSDM presents new challenges for both clustering analysis and consensus building. Minimum-cost consensus (MCC) based on the optimization principle is widely recognized as an effective tool for managing the consensus-reaching process. However, there is a scarcity of literature that addresses the study of MCC within the context of LZ-LSDM, as well as the application of MCC in the identification and treatment of non-cooperative behaviors. To this end, this study proposes a punishment strategy-driven multi-stage type-α constrained MCC model for LZ-LSDM problems. First, a similarity constraint-based clustering method with linguistic Z-numbers is proposed. Given the clustering results, a type-α constrained MCC (α-CMCC) model with personalized feedback constraints is designed to provide a personalized solution for visualizing opinion adjustment and preventing over-adjustment. Based on the optimal solution obtained by α-CMCC, the identification rule for non-cooperative behaviors is proposed. We conclude three punishment strategies—namely, pure, mixed, and cross—to address non-cooperative behaviors by arranging and combining commonly used punishment approaches. Finally, we illustrate the feasibility and validity of the model through a case study designed to facilitate consensus among an online patient community on knowledge-based recommendations. An exhaustive comparative analysis reveals the advantages and features of the proposed consensus model.

A dual-level consensus model for large-scale group decision-making driven by trust relationships in social networks

In large-scale group decision-making, obtaining reasonable and reliable decision results is the research focus. To this end, a dual-level consensus model for large-scale group decision-making is constructed. In the model, we first presented a method for measuring the two-dimensional similarity between decision-makers. The method considered the opinion similarity at evaluation scales and ranking results level, which provided a more comprehensive measure of the opinion differences between decision-makers. A hierarchical clustering algorithm based on the two-dimensional similarity-trust matrix is proposed. By considering the two-dimensional similarity and trust relationships among decision-makers, the method contributed to the formation of subgroups with excellent cooperative and communicative atmospheres. Furthermore, we introduced a dual-level consensus measures method to comprehensively and accurately assess the group consensus level. To increase the efficiency of decision-making and the satisfaction of decision-makers, we proposed a personalized feedback adjustment mechanism for decision-makers. Additionally, for the subgroups that have not reached consensus, a social networks DeGroot model is constructed to adjust the opinion of subgroups. Finally, a case study of low-carbon supplier selection verified the feasibility and applicability of the model. The effectiveness and superiority of the model are demonstrated by comparative and simulation analysis. The analysis results show that the model is conducive to promoting cooperation and coordination among decision-makers and obtaining highly acceptable and quality decision results.

Key components identification of EMU complex system faults with interval intuitionistic fuzzy set and multi-attribute group decision-making based on FMECA method

With the continuous acceleration of high-speed railway, the high-voltage traction system of the EMU is an important part for ensuring the operation speed and safety. If the failure does not discontinued effectively, it will cause major dangerous accidents, so the key components identification of system is crucial. This paper focus on the contradictions of the expert evaluation information ambiguity, the difference of expert risk appetite and the rationality of risk priority number (RPN) calculation method in the traditional failure analysis method FMECA. The interval intuitionistic fuzzy set (IIFS) is introduced to transform the expert evaluation into the form of membership interval and non-membership interval, which reduced the ambiguity of the specific numerical score. The interval intuitive fuzzy entropy was used to determine the entropy values of the occurrence (O), severity (S), and undetectable degree (D) of each failure mode under each expert score, which was used to calculate the weight value [Formula: see text], to weaken the influence caused by subjective risk preference. The interval intuition fuzzy ensemble operator (AIVIFWM) is used to assemble a single scoring matrix into a comprehensive score, which weakens the subjective influence of expert evaluation. Combined with the multi-attribute group decision-making idea, the score function [Formula: see text] is calculated for each comprehensive evaluation interval of each failure mode after assembly, so as to sort the failure mode risk and finally identify the key components. Based on the fault data of the high-voltage traction system of a certain type of EMU in 2022, 39 failure modes of 30 components are researched and summarized. The results show that rectifier, converter cooling unit, and carbon skateboard are the key components of EMU high-voltage traction system, which provided basic support for the detection and maintenance decision.

A Large-Scale Group Decision-Making Model Considering Expert Authority Degree and Relationship Evolution Under Social Network

In Large-Scale Group Decision-Making (LSGDM), effectively implementing consensus models is pivotal for managing decision complexity. While trust-based LSGDM has garnered attention, there remains a need for deeper insights into the dynamics of interexpert trust and the impact of authority effects on the decision-making process. This study introduces a sophisticated model for large-scale group decision-making, incorporating considerations of expert “trustworthiness-authority.” Initially, the study assesses the trustworthiness of experts based on social network relationships and opinion similarity while using background information and consensus levels to establish their authority. Subsequently, experts are categorized into four distinct regions based on their trustworthiness and authority assessments. Furthermore, tailored consensus adjustment methods are proposed for each region based on social contagion theory to facilitate consensus achievement. Additionally, a case study is conducted to demonstrate the rationality and effectiveness of the proposed LSGDM model, considering expert “trustworthiness-authority.” Finally, the necessity and superiority of the proposed model are further verified through comparison analysis and sensitivity analysis.

The Influence of CEO's Anger on Investors' Group Decision-Making

The Influence of CEO's Anger on Investors' Group Decision-Making

Assessment of bio-medical waste disposal techniques using interval-valued q-rung orthopair fuzzy soft set based EDAS method

Selecting an optimum technique for disposing of biomedical waste is a frequently observed obstacle in multi-attribute group decision-making (MAGDM) problems. The MAGDM is commonly applied to tackle decision-making states originated by obscurity and vagueness. The interval-valued q-rung orthopair fuzzy soft set is a novel variant of fuzzy sets. The main objective of this study is to introduce the interval-valued q-rung orthopair fuzzy soft Einstein-ordered weighted and Einstein hybrid weighted aggregation operators. Based on developed aggregation operators, a novel decision-making approach, the Evaluation based on the Distance from the Average Solution introduced to solve the MAGDM problem. The execution of the proposed approach demonstrates the significant impact of determining the most effective strategy to handle biomedical waste. Our proposed approach's practicality is confirmed by a case study focusing on selecting the most effective technique for Biomedical Waste (BMW) treatment. This study shows that autoclaving is the most effective method for the disposal of BMW. Comparative and sensitivity analysis confirms the consistency and effectiveness of our methodology. The comparative study indicates the effects of the proposed strategy are more feasible and realistic than the prevailing techniques.

Enhanced Condition Monitoring of Gearboxes using Fuzzy Comprehensive Evaluation and Group Decision-making: A Case Study

In this study, we introduce a novel methodology for the condition monitoring of gearboxes by integrating fuzzy comprehensive evaluation and group decision-making. Traditional risk assessment methods often suffer from subjective biases and uncertainties inherent in expert evaluations. Our approach mitigates these limitations by aggregating expert opinions using fuzzy membership functions and assigning weights based on the similarity of individual evaluations to the group consensus. This converts qualitative judgments into quantitative measures, resulting in more precise and objective Risk Priority Numbers (RPNs). We validate the efficacy of our methodology through a case study involving a gearbox. The primary failure modes identified include gear tooth wear, misalignment, bearing failure, lubrication failure, and thermal overload. Our results indicate a significant improvement in condition monitoring accuracy, with calculated fuzzy RPN values closely aligning with historical data and expert feedback. Comparative analysis highlights the advantages of our methodology over conventional RPN calculations, particularly in reducing subjective biases and enhancing the reliability of risk assessments. Our findings demonstrate that this methodology can be effectively applied in various industrial settings, establishing a robust framework for mechanical system condition monitoring. Future research should explore integrating advanced data analytics and machine learning techniques to enhance the methodology's accuracy and efficiency.

Prioritization of sustainable development plannings for supply chain finance using a novel Triangular neutrosophic-based TOPSIS-CPT-VIKOR model

The development of sustainable supply chain finance (SSCF) promotes the improvement of the long-term production and operational performance of enterprises. It is significant to explore both environmentally friendly and cost-effective solutions, especially in the presence of uncertain information. The triangular neutrosophic number (TrNN) can enable decision-makers (DMs) to express their preferences in high uncertainty. This paper aims to provide a systematic multiple-attribute group decision-making (MAGDM) framework to support DMs in prioritizing sustainable development plannings by combining the best-worst method (BWM), optimization model of maximizing deviation method, and uniformity entropy theory. Moreover, a TrNN-based extended TOPSIS method is developed to measure the differences in DMs’ importance. We incorporate the cumulative prospect theory (CPT) method to account for DMs’ psychological behaviors in risk scenarios. The CPT-VIKOR methodology is employed to rank alternatives, enhancing the precision of decision-making. Finally, the proposed approach is successfully applied to the LNG industry to illustrate its applicability.

An Online Review Data-Driven Fuzzy Large-Scale Group Decision-Making Method Based on Dual Fine-Tuning

Large-scale group decision-making (LSGDM) involves aggregating the opinions of participating decision-makers into collective opinions and selecting optimal solutions, addressing challenges such as a large number of participants, significant scale, and a low consensus. In real-world scenarios of LSGDM, various challenges are often encountered due to factors such as fuzzy uncertainties in decision information, the large size of decision groups, and the diverse backgrounds of participants. This paper introduces a dual fine-tuning-based LSGDM method using an online review. Initially, the sentiment analysis is conducted on online review data, and the identified sentiment words are graded and quantified into a fuzzy data set to understand the emotional tendencies of the text. Then, the Louvain algorithm is used to cluster the decision-makers. Meanwhile, a method combining Euclidean distances with Wasserstein distances is introduced to accurately measure data similarities and improve clustering performances. During the consensus-reaching process (CRP), a two-stage approach is employed to adjust the scores: to begin with, by refining the scores of the decision representatives via minor-scale group adjustments to generate a score matrix. Then, by identifying the scores corresponding to the minimum consensus level in the matrix for adjustment. Subsequently, the final adjusted score matrix is integrated with the prospect–regret theory to derive the comprehensive brand scores and rankings. Ultimately, the practicality and efficiency of the proposed model are demonstrated using a case study focused on the purchase of solar lamps. In summary, not only does the model effectively extract the online review data and enhance decision efficiency via clustering, but the dual fine-tuning mechanism in the model to improve consensus attainment also reduces the number of adjustment rounds and avoids multiple cycles without achieving the consensus.

Bounded rationality consensus reaching process with prospect theory and preventing individual weight manipulation for multi-attribute group decision making

Owing to the development and popularization of social media, multi-attribute group decision-making (MAGDM) adopting social network analysis (SNA) has appealed to widespread focus for a few years. However, most existing research on SNA has not simultaneously considered the expert's weight manipulation behavior and bounded rationality during the consensus-reaching process (CRP). To overcome this limitation, an original consensus model predicated on prospect theory (PT) and preventing individual weight manipulation is proposed in this paper. First, the experts’ behavior under certain risks is characterized by using PT, where a dynamic reference point is proposed to provide a more objective description of the experts’ psychological behavior. Then, the PT is integrated into the CRP, and the three-level prospect consensus measurement and a three-stage feedback identification mechanism are investigated. Subsequently, to safeguard against individual weight manipulation and obtain the appropriate expert weights, a feedback model incorporating minimum adjustment and maximum entropy rooted in PT is developed. This model is established from the perspectives of “efficiency” and “equity.” At last, the proposed method is applied to evaluate low-carbon investment projects in a power generation enterprise to verify its effectiveness and applicability. The superiority and robustness of the proposed method are also demonstrated through comparative analysis and sensitivity analysis.

Semantic differences and psychological behavior in multi-criteria group decision-making: Do they need consideration?

In Multi-Criteria Group Decision-Making (MCGDM), linguistic evaluation is widely used due to its flexibility. In particular, Personalized Individual Semantics (PIS), namely decision-makers hold heterogeneous understandings of the same word, has been extensively studied to elicit numeric meaning to the linguistic scale. However, few studies consider the need to tailor linguistics to the different indicators used, as well as the semantic difference in the same level linguistic that experts use when situations change. The traditional PIS models overlook the psychological aspect of the decision makers. To solve these problems, this paper proposes a method that considers semantic differences arising from the decision-makers and the indicators and incorporates the experts' psychological behavior. We apply this method to assess the alternatives for a smart refrigerator product service system. The results suggest that a large semantic difference, when evaluating the linguistic indicators, can influence the final outcome. We validate the flexibility and the effectiveness of the method by comparing against the base case of no semantic difference, using the Euclidean and Manhattan distance measures.

An Integrated T-Spherical Fuzzy Einstein Interaction Aggregator Group Decision-Making Approach: A Case Study of Concrete 3D Printing Robot Application in Vietnam

This study introduces the integrated T-spherical fuzzy Einstein interaction aggregator group decision-making approach, a novel framework designed to enhance multi-criteria decision-making (MCDM). Implementing the case study of concrete 3D printing technology in Vietnam, this approach integrates T-spherical fuzzy sets with Einstein aggregation operators to handle the complexities of uncertain and subjective expert judgments effectively. The methodology provides a robust mechanism for evaluating and prioritizing the barriers and strategies associated with the implementation of concrete 3D printing. Findings from this study underline the significance of technological advancements and strategic financial incentives, with R&D strategy emerging as the top priority. This research contributes to both theoretical advancements in decision-making frameworks and offers practical insights for industries looking to integrate emerging technologies. Moreover, it demonstrates the application of advanced fuzzy set theories in real-world settings, providing a valuable tool for decision-makers facing similar technological adoption challenges.

Application of multi-criteria group decision-making for water quality management.

As waste discharge into numerous river systems escalates, the pollution of water bodies typically rises. Given the limited capacity of rivers to withstand pollution and their constrained self-cleaning capabilities, treated pollutants from waste discharge must be released into the river. Despite numerous models and algorithms proposed for managing river water quality to meet standards, literature, to our awareness, lacks the utilization of a comprehensive multi-criteria group decision-making approach for water quality management, particularly in river systems. Therefore, this research introduces a new, comprehensive multi-criteria group decision-making for the management of water quality in the Haraz River basin, located in Iran. To do so, the water quality of the basin, a one-dimensional water quality model, QUAL2Kw, was employed to simulate and calibrate the water quality along the river. The simulation results revealed that the downstream water quality violates the water quality standards. To mitigate this issue, various scenarios for waste load allocation (WLA) were evaluated, including no wastewater treatment, primary wastewater treatment, advanced secondary wastewater treatment utilizing the activated sludge (AS) method, and advanced wastewater treatment via the membrane bioreactor (MBR) method. Utilizing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Fuzzy TOPSIS group decision-making model, it was determined that the optimal solution was the implementation of secondary wastewater treatment utilizing the activated sludge method for the 11 PS of pollution, while still adhering to Iranian water quality standard. In addition, the findings of the present study indicate that the implementation of primary wastewater treatment, advanced secondary wastewater treatment utilizing AS, and advanced wastewater treatment through MBR within the study area led to a significant enhancement in water quality. This enhancement ranged from 35 to 105% across various scenarios when compared to conditions where no actions were taken to the treatment of water.

Exploiting game equilibrium mechanisms towards social trust-based group consensus reaching

In group decision making (GDM), the real or virtual moderator usually provides the suggestion and compensation based on consensus improvement for the decision maker (DM) to promote group consensus. In order to ensure DMs own interests, there are the game among DMs and the game between DM and moderator to effect the consensus reaching. In this case, we analyze the inherent game reasons and investigate the game-driven consensus model. Firstly, based on the subjective trust of social network and the evaluation consensus of DMs, and inspired by the idea of data envelopment analysis (DEA), this paper explores the quantitative relation between subjective trust and the DMs’ consensus to determine the DM’s weights. Then, under the compensation of consensus improvement, we formulate game strategy between DMs and the moderator to ensure the DM’s consensus, which impacts the cooperation behavior between DM and the moderator. Based on the game strategy, we further establish bi-level game consensus model and investigate Nash equilibrium of group consensus model. Finally, the experiments are used to illustrate the feasibility of the proposed method, and comparative analysis demonstrates its advantages.

A multiple attribute group decision making model based on 2-tuple linguistic pythagorean fuzzy dombi aggregation operators for optimal selection of potential global suppliers

Multiple-Attribute Group Decision-Making (MAGDM) is a significant area of research in decision-making, and its principles and methodologies are widely implemented. A Pythagorean Fuzzy Set (PFS) is an extension of an Intuitionistic Fuzzy Set (IFS) that is highly valuable for representing uncertain information in real-world scenarios. The 2-Tuple Linguistic Pythagorean Fuzzy Number (2TLPFN) is a specific type of Pythagorean Fuzzy Number (PFN) that can be used to represent uncertainty in real-world decision making through the use of 2-Tuple Linguistic Terms (2TLTs). This paper focuses on the examination of Multiple Attribute Group Decision Making (MAGDM) using 2TLPFNs. Dombi's t-norm and t-conorm operations were commonly referred to as Dombi operations, which might have been greater degree of applicability if offered in a new form of flexibility within the general parameter. In this research, we implement Dombi operations to construct some 2-Tuple Linguistic Pythagorean Fuzzy (2TLPF) Dombi Aggregation operators. These operators include the 2TLPF Dombi Weighted Averaging (2TLPFDWA) operator, 2TLPF Dombi Ordered Weighted Averaging (2TLPFDOWA) operator, 2TLPF Dombi Weighted Geometric (2TLPFDWG) operator, and 2TLPF Dombi Ordered Weighted Geometric (2TLPFDOWA) operator. An analysis is conducted to examine the unique characteristics of these suggested operators. Subsequently, we leveraged the proposed operators to develop a model aimed at tackling the MAGDM problems in the 2TLPF environment. Eventually, a suitable instance has been demonstrated to validate the formation of the model as well as exhibit its implementation and resilience.

A fuzzy reliability assessment methodology for city gas stations based on an extended T-S fault tree

City gas stations (CGSs) play a crucial role in ensuring a stable and safe supply of natural gas to urban users. However, as the service time of stations increases and the performance of components deteriorates, concerns about the safety and reliability of these station have grown among operators and local government authorities. This paper proposes a fuzzy reliability assessment methodology for CGSs that considers the polymorphism of component faults and the uncertainties associated with fault relationships, failure probabilities, and fault magnitudes. The methodology utilizes T-S fuzzy gates to describe the correlation among events and constructs a T-S fuzzy fault tree for CGSs. Component fault states are represented using fuzzy numbers, and a fuzzy group decision-making approach is introduced to evaluate the current fault magnitude of components. To handle the uncertainty caused by sparse failure sample data, a Bayesian updating estimation method is presented to estimate the failure probabilities of components. Furthermore, T-S fuzzy importance analysis is applied to identify the weak points in the CGS system. The effectiveness of the developed methodology is demonstrated through a case study of reliability analysis of a city gas distribution station. The research findings provide valuable support for optimizing the design and implementing preventive maintenance of CGSs.

Electricity Sales Package Decision Making Using Two-Stage Density Clustering and Minimum Adjustment Distance Consensus

In the decision making of electricity sales packages, it is usually the specific situation of similar customers that provides the basis of a decision-making plan for target customer package selection, so it is particularly important to integrate the opinions of similar customers. Therefore, a multi-attribute group decision-making method for an electricity sales package is proposed, which is based on two-stage density clustering (TSDC) and minimum adjustment distance consensus. Firstly, in order to provide support for identifying similar customers among target customers, a sample customer set clustering method is proposed, which is based on a customer portrait label system and TSDC. Secondly, based on the entropy method, the attribute weight of the electricity sales package is determined. Based on the weight and the multi-attribute group decision-making consensus process, the minimum adjustment distance consensus of the sample customers’ fuzzy evaluation matrix for the electricity sales package is proposed. Then, a full-ranking decision method for an electricity sales package based on target customer satisfaction is proposed. Finally, customers in a certain area of China are selected as an example. This example is used to verify the accuracy and effectiveness of the decision-making method of electricity sales package proposed in this paper.

Projection-driven optimization framework for risk assessment of “dual carbon” audit in power grid enterprises with q-rung orthopair fuzzy MAGDM

Power grid enterprises are the backbone of promoting clean and low-carbon energy transformation, playing an important role in achieving carbon peak and carbon neutrality. It is very necessary to audit the implementation of the “dual carbon” work of power grid enterprises, in order to better implement the national “dual carbon” policy and serve the development of the national economy. The risk assessment of “dual carbon” audit in power grid enterprises is multiple-attribute group decision-making (MAGDM). In this study, in light with projection measure technique and bidirectional projection measure technique, four forms of projection measure technique with q-rung orthopair fuzzy sets (q-ROFSs) are conducted. Then, two weighed projection techniques are conducted to manage the MAGDM. Finally, a numerical example for risk assessment of “dual carbon” audit in power grid enterprises and comparative analysis is utilized to verify the developed techniques. The major contribution of this research is constructed: (1) entropy technique is implemented to determine the weight values in line with score number (SN) and accuracy number (AN); (2) two weighed projection techniques are implemented to put forward MAGDM with q-ROFSs; (3) the numerical example for risk assessment of “dual carbon” audit in power grid enterprises is implemented to show the two weighed projection techniques under q-ROFSs; and (4) comparative studies are constructed with existing techniques.

A generalized TODIM evaluation approach based on the novel score function and trust network under interval-valued hesitant fuzzy environment

In multi-attribute group decision-making (MAGDM), the social relationships among group members, professional backgrounds and psychological behaviors of decision makers are important factors for achieving high-quality group decisions. However, realizing the consideration of these factors in fuzzy and complex group decision-making scenarios is an important and urgent issue. In MAGDM problems, interval-valued hesitant fuzzy sets (IVHFSs) can better show the hesitant and fuzzy uncertainty information because they are an extension of fuzzy sets. This paper focuses on the score function of IVHFSs and develops a generalized TODIM evaluation method utilizing the novel score functions of IVHFEs and trust networks. The developed method effectively realizes the application of these factors in fuzzy and complex group decision making scenarios. In this contribution, a new deviation function for IVHFSs is created in this paper. It takes into account two types of deviation that are common in IVHFSs: the ambiguity of interval width and the hesitation and uncertainty of multiple intervals. In the sequel, a novel score function for IVHFSs is developed with help of the novel deviation function and the traditional score function. Concerning the perspective of group consistency, a model for determining expert weights is constructed using quality parameters, which combine the novel score function with the trust network. A subjective and objective comprehensive weighting model is made to get attribute weights based on how certain decision information is and the experts’ subjective experience. This is done because the more uncertain something is, the higher the risk. Eventually, we develop a generalized TODIM technique for MAGDM based on the novel score function and trust network in IVHFS environment. The developed approach is demonstrated with a preventive engineering decision-making problem. Sensitivity and comparative analyses are implemented to demonstrate the superiority, stability, and validity of the proposed method in this paper. According to the findings of this study, we conclude that the proposed methodology offers a comprehensive and systematic approach to evaluate MAGDM in the context of IVHFS with trust networks.

An Improved Laplacian Gravity Centrality-Based Consensus Method for Social Network Group Decision-Making with Incomplete ELICIT Information

With the advancement of information technology, social media has become increasingly prevalent. The complex networks of social relationships among decision-makers (DMs) have given rise to the problem of social network group decision-making (SNGDM), which has garnered considerable attention in recent years. However, most existing consensus-reaching methods in SNGDM only consider local network information when determining the influence of DMs within the social network. This approach fails to adequately reflect the crucial role of key DMs in regulating information propagation during the consensus-reaching process. Additionally, the partial absence of linguistic evaluations in the decision-making problems also poses obstacles to identifying the optimal alternative. Therefore, this paper proposes an improved Laplacian gravity centrality-based consensus method that can effectively handle incomplete decision information in social network environments. First, the extended comparative linguistic expressions with symbolic translation (ELICIT) are utilized to describe DMs’ linguistic evaluations and construct the incomplete decision matrix. Second, the improved Laplacian gravity centrality (ILGC) is proposed to quantify the influence of DMs in the social network by considering local and global topological structures. Based on the ILGC measure, we develop a trust-driven consensus-reaching model to enhance group consensus, which can better simulate opinion interactions in real-world situations. Lastly, we apply the proposed method to a smart city evaluation problem. The results show that our method can more reasonably handle incomplete linguistic evaluations, more comprehensively capture the influence of DMs, and more effectively improve group consensus.