Large-scale Group Decision Research Articles

A Confidence and Conflict-Based Consensus Reaching Process for Large-Scale Group Decision-Making Problems With Intuitionistic Fuzzy Representations

A Confidence and Conflict-Based Consensus Reaching Process for Large-Scale Group Decision-Making Problems With Intuitionistic Fuzzy Representations

Mining Maximum Ordinal–Cardinal Consensus for Large-Scale Group Decision Making With Incomplete Fuzzy Preference Relations

Mining Maximum Ordinal–Cardinal Consensus for Large-Scale Group Decision Making With Incomplete Fuzzy Preference Relations

Adaptive opinion evolution process with opinion dynamics for large-scale group decision making: A novel approach based on overlapping community detection in social networks

The rapid development of Web2.0 technologies and social media platforms has developed a new paradigm that allows many individuals to participate in decision-making processes within online social networks, leading to the rise of social network group decision making (SNGDM). Existing research in SNGDM primarily focus on small-scale DMs, which may not be suitable for large-scale SNGDM problems due to the high costs and time constraints of adjustments. Moreover, the non-overlapping community structure in social network encounter several limitations, further complicating the resolution of large-scale SNGDM problems. In this study, we propose an adaptive opinion evolution process with opinion dynamics for large-scale SNGDM. The proposed approach comprises four stages: classification of decision makers (DMs), determination of community weights, consensus reaching process, and alternative selection. A real-world application is utilized to demonstrate the effectiveness of the proposed method, and a comparison with existing related works highlights the advantages and innovation of the proposed model.

Addressing multi-attribute large-scale group decision making with overlapping subgroups: A bidirectional adjustment consensus reaching method for heterogeneous decision makers

Due to the rapid development of internet technology, multi-attribute large-scale group decision making (MALSGDM) becomes a research hotspot of multi-attribute group decision making. An interesting phenomenon is that decision makers (DMs) may assume multiple roles across various interest groups, thereby naturally forming overlapping subgroups. Aiming at the MALSGDM problem with overlapping subgroups, this paper proposes an overlapping subgroup-based consensus reaching method considering heterogeneous DMs. Firstly, a novel overlapping clustering method is proposed to decompose the large-scale group into several overlapping subgroups characterized by low evaluation conflict and stable trust relationship, thereby identify ordinary and key DMs. Secondly, a two-criteria consensus reaching judgment method is devised as the termination condition for the consensus reaching process. Thirdly, a novel bidirectional adjustment method considering heterogeneous DMs is constructed, which focuses on key DMs belonging to multiple subgroups simultaneously. The adjusted evaluation values are obtained through linear uncertain minimum adjustment consensus models under two different cases. Finally, a numerical example is provided to demonstrate the feasibility of the proposed method, and the comparative and sensitivity analyses are carried out to verify the superiority of the proposed method.

Adaptive Bi-directional Consensus Reaching Model with Social Influence Evolution for Large-Scale Group Decision-Making with an Application to Observation Scheme Selection

Adaptive Bi-directional Consensus Reaching Model with Social Influence Evolution for Large-Scale Group Decision-Making with an Application to Observation Scheme Selection

A trust relationship network-based consensus model in large-scale TODIM group decision-making

PurposeIn large-scale group decision-making (LSGDM) situations, existing TODIM group decision-making methods often fail to account for the influence of social network relationships and the bounded rationality of decision-makers (DMs). To address this issue, a new TODIM-based group decision-making method is proposed that considers the current trust relationships among DMs in a large-scale trust relationship network.Design/methodology/approachThis method consists of two main stages. In the first stage, the large-scale group is partitioned into several sub-clusters based on trust relationships among DMs. The dominance degree matrix of each sub-cluster is then aggregated into the large-scale group dominance degree. In the second stage, after aggregating the large-scale group dominance degree, the consensus index is calculated to identify any inconsistent sub-clusters. Feedback adjustments are made based on trust relationships until a consensus is reached. The TODIM method is then applied to calculate the corresponding ranking results. Finally, an illustrative example is applied to show the feasibility of the proposed model.FindingsThe proposed method is practical and effective which is verified by the real case study. By taking into account the trust relationships among DMs in the core process of LSGDM, it indeed has an impact on the decision outcomes. We also specifically address this issue in Chapter Five. The proposed method fully incorporates the bounded rationality of DMs, namely their tendency to accept the opinions of trusted experts, which aligns more with their psychology. The two-stage consensus model proposed in this paper effectively addresses the limitations of traditional assessment-based methods.Originality/valueThis study establishes a two-stage consensus model based on trust relationships among DMs, which can assist DMs in better understanding trust issues in complex decision-making, enhancing the accuracy and efficiency of decisions, and providing more scientific decision support for organizations such as businesses and governments.

Evaluating edge artificial intelligence-driven supply chain management platforms using collaborative large-scale fuzzy information fusion

The rapid development of edge artificial intelligence (AI) has brought about revolutionary changes in supply chain management (SCM). It not only provides real-time data processing capabilities but also accelerates the decision-making process, injecting more innovative elements into SCM. In this context, SCM platforms require new technological developments and effective evaluations, as they collectively drive the efficient coordination of complex business processes. SCM platforms enable the seamless coordination of various supply chain elements, facilitating streamlined operations and enhanced decision-making processes. The evaluation of these platforms not only serves to validate their performance and effectiveness, but also contributes to continual improvements in design and application, and addressing the evolving demands within the realms of global commerce and dynamic market conditions. The ongoing assessment and enhancement of SCM platforms play a crucial role in optimizing resource allocation, improving production efficiency, and fostering adaptability to changing market dynamics. However, the acquisition of assessment data often introduces imprecise data. Additionally, the large-scale nature and bounded rationality of decision-makers (DMs) significantly impact the evaluation of SCM platforms. This article aims to explore a collaborative large-scale information fusion approach and provide a robust fuzzy framework for evaluating SCM platforms. This approach employs a combination of spherical fuzzy sets (SFSs), large-scale group decision-making (LSGDM), behavioral theories and three-way decisions (TWD) to thoroughly explore collaborative large-scale information fusion and its practical application in assessing SCM platforms. It introduces an innovative spherical fuzzy (SF) LSGDM technique and integrates TWD with the inclusion of prospect theory (PT) and regret theory (RT) to mitigate potential decision risks. The developed collaborative large-scale information fusion approach is assessed for validity, effectiveness and practicality in the context of evaluating SCM platforms using online data. Experimental results demonstrate that this approach provides reasonable evaluation outcomes, considering uncertain information processing capabilities, large-scale characteristics, bounded rationality and decision risks.

A Binary Risk Linguistic Fuzzy Behavioral TOPSIS Model for Multi-attribute Large-Scale Group Decision-Making Based on Risk Preference Classification and Adaptive Weight Updating

A Binary Risk Linguistic Fuzzy Behavioral TOPSIS Model for Multi-attribute Large-Scale Group Decision-Making Based on Risk Preference Classification and Adaptive Weight Updating

A maximum satisfaction consensus-based large-scale group decision-making in social network considering limited compromise behavior

With the enrichment of social platforms, large-scale group decision-making (LSGDM) in social networks has gradually taken shape. In practice, experts may show limited compromise behavior during consensus reaching process (CRP), which may lead to bias or even failure in decision-making results. To tackle this issue, considering limited compromise behavior, this paper proposes a maximum satisfaction consensus (MSC) model, and focusing on LSGDM with fuzzy preference relation in social networks. Firstly, an expert weight determination algorithm integrating the interactive weights and individual weights is constructed. The interactive weights are determined by social influence strength, while the individual weights are measured by the compromise degree of limited compromise behavior and the consistency level of preferences. Subsequently, a grey clustering method is developed to classify experts into subgroups, considering both opinions similarity and limited compromise behavior similarity. In CRP, considering the limited compromise behavior, an MSC model under the limited budget determined by the minimum cost consensus (MCC) model is proposed. Based on MSC and MCC models, satisfaction-cost ratio is defined to measure the efficiency of CRP. Finally, a case study of selecting of shared bicycle operators and further discussion are conducted to verify the feasibility and superiority of the study.

Location Selection for Dry Hot Rock Exploration Based on Large-Scale Group Decision-Making with Three-way Decision

Location Selection for Dry Hot Rock Exploration Based on Large-Scale Group Decision-Making with Three-way Decision

Large-scale group hierarchical DEMATEL method with automatic consensus reaching

Decision-making trial and evaluation laboratory (DEMATEL) is widely used because of its ability to effectively analyze nonlinear relationships between factors in complex systems. With the increasing complexity of decision-making problems, large-scale group decision-making (LSGDM) has become the norm. Most existing DEMATEL methods are only suitable for small-scale groups and simple systems. This study, therefore, proposes a large-scale group hierarchical DEMATEL method that considers consensus reaching. The DEMATEL method for LSGDM faces three challenges: large differences in knowledge structures, difficulty coordinating expert opinions, and slow group-consensus convergence. To address these challenges, first, we use hierarchical decomposition to decompose the complex system into simple systems with different levels to reduce the difficulty of decision-making in complex systems. Second, considering the limitations of expert knowledge and experience, we use the basic probability assignment function to extract the opinions of experts at different levels of subsystems and factors. Third, we divide experts into different clusters using K-means clustering to solve the problem of difficult expert-opinion coordination. Fourth, we design two types of consensuses (intrasubgroup and intersubgroup consensus) and an efficient new type of opinion autocorrection mechanism to solve the problem of the slow convergence of intragroup consensus and improve the efficiency of consensus reaching. Finally, we demonstrate the superiority of the proposed method through data analysis and method comparison.

Optimizing-Information-Granule-Based Consensus Reaching Model in Large-Scale Group Decision Making

Optimizing-Information-Granule-Based Consensus Reaching Model in Large-Scale Group Decision Making

A sentiment analysis and dual trust relationship-based approach to large-scale group decision-making for online reviews: A case study of China Eastern Airlines

With the rapid development of e-commerce, more and more people are willing to post their reviews and opinions about the products they buy online. Therefore, the use of online review data for decision-making appears to be more practical and universal, and how to effectively use this kind of data to support large-scale group decision-making (LSGDM) is a worthy research direction. In this paper, we firstly use sentiment analysis to analyze online review data to derive the decision maker's (DM's) sentiment value, and apply the sentiment value to construct a social network based on a dual trust relationship, which considers both familiarity-based trust and similarity-based trust. Secondly, a directed Louvain clustering algorithm in light of dual trust relationships and a method for solving the DM's intra-group weights and the group's weights are proposed based on this network. A two-stage clustering based consensus model in light of dual trust relationships is then proposed, in which the DMs in the agreement cluster can communicate with other DMs outside of such a cluster and dynamically update the grouping using the clustering algorithm. Finally, the practicality and effectiveness of the LSGDM method proposed in this paper are verified through a real case.

A robust incomplete large-scale group decision-making model for metaverse metro operations and maintenance

The metaverse, constructed through digital technology, serves as a virtual realm intertwining with reality. Within this context, the challenge of evaluating data from diverse sources arises, and the application of large-scale group decision-making (LSGDM) methods emerges as a viable solution. Handling incomplete information and reducing dimensionality for large-scale decision-makers (DMs) is crucial in addressing complex decision-making problems. Moreover, addressing missing data is a fundamental and pivotal concern in tackling real-world decision challenges, given the ubiquitous presence of information gaps that cannot be straightforwardly integrated into decision models. Besides, the intricacies of LSGDM amplify this challenge by introducing a wealth of DMs, thereby augmenting the complexity and diversity of decision-related information. This paper proposes an approach to supplement missing data by double-dimensions. This paper explores various facets of similarity relationships within the data to enhance data completeness. Additionally, this paper categorizes DMs into clusters based on their relevance and establishes a two-stage consensus-reaching process (CRP) that takes into account both group sizes and individual consensus contributions. These CRPs play a crucial role in enhancing the overall consistency and consensus within the decision group. Subsequently, this paper applies a robust decision-making method rooted in MULTIMOORA (Multi-Objective Optimization by Ratio Analysis plus the complete MULTIplicative form) to rank decision objects. Finally, this paper employs this proposed methodology in a practical case study that involves evaluating the operational status of a metaverse’s urban construction metro system. Following these considerations, a comprehensive stability analysis of relevant parameters is conducted to guarantee the robustness and reliability of the decision-making process.

New consensus reaching process with minimum adjustment and feedback mechanism for large-scale group decision making problems under social trust networks

Large-scale group decision making problems in social networks environment have become an important research topic in decision making science. In this paper, we propose a new consensus reaching process with minimum adjustment and feedback mechanism considering public opinions for large-scale group decision making problems under social trust networks. The trust propagation operator based on Archimedean t-norm is used to calculate incomplete trust values first. The fast unfolding algorithm is used to divide experts into subgroups. The opinion evolution model is presented to calculate experts’ weights based on dynamic trust values and expert influence. Public opinions are used to determine consensus thresholds of attributes. A dual-path adjustment model is developed for group consensus in social network. Divide experts’ evaluation values into three different types: ‘conflict behavior’, ‘tolerant behavior’, ‘trust behavior’. Minimum cost adjustment models have been developed considering adjustment directions for the ‘conflict behavior’ and interactive path is activated for the ‘tolerant behavior’. The drugs selection from E-commerce platform for therapy of influenza at home is used to illustrate the new method and comparisons have also been conducted.

Large-scale group decision-making involving community representatives: A perspective of combining strong and weak ties

In the era of social media, the issue of large-scale group decision-making (LSGDM) is becoming increasingly prominent. The complexity of large group interactions increases rapidly with the expansion of the group size. Current research has often used cluster analysis to reduce the dimensionality of LSGDM, but the decision agents following dimensionality reduction are not clearly defined, which hinders the practical application of LSGDM methods. This paper studies the LSGDM through the context of community representatives which are determined by combining strong and weak ties. The participation of such representatives in subsequent decision-making can reduce negotiation costs and efficiently form a consensus from both global and local perspectives. To identify these community representatives, this study improves the traditional Laplacian centrality, calculates the local and global centrality of individuals, and defines metrics for measuring representatives. The opinion dynamics model is used to verify the effectiveness of the LSGDM model with community representatives. The karate club network is used to illustrate the application of the proposed LSGDM model. Simulation and comparative analysis results show that the proposed model takes less time to reach consensus than traditional ones. Furthermore, there is no complete linear relationship between the representative size and the time taken to reach group consensus.

Influence measure-based large-scale group decision making with linear uncertain preference relations

This research focuses on the problem of large-scale group decision-making (LSGDM) based on influence measure under linear uncertain preferences. The value of this research is that it improves the performance of the current clustering algorithms, increases the efficiency of consensus reaching for major decision-making events, thus reducing the cost of feedback adjustments, and at the same time reflecting the risk attitude of the decision-makers (DMs) during situations of uncertainty. First, an approach for measuring the influence of the DMs is presented. Based on the influence measure, a clustering method is proposed to classify the experts into subgroups. Then, a method for determining the weights of the subgroups is developed by combining the influence and credibility of the subgroup leader as well as the intra-subgroup consensus level. Subsequently, a feedback adjustment method is provided to reduce the disagreement among the DMs by considering the unit adjustment cost and the corresponding adjustment willingness of the DMs. Further, based on the trust risk and credibility of the DMs, a dual management mechanism of non-cooperative behavior is established to hasten consensus. Finally, a case study is presented to demonstrate the practicality of the suggested approach, while simulation experiments and comparative analysis are conducted to verify the model.

An approach for reaching consensus in large-scale group decision-making focusing on dimension reduction

Group decision-making and consensus modeling have always been important research topics. With the widespread use of the Internet, group decisions can be made online, in which a large number of decision-makers participate. Most of the existing studies on large-scale group decision-making consider 20–50 decision-makers. Therefore, there is a need for a framework that focuses on situations where thousands of decision-makers exist. As dimension reduction is one of the five primary challenges in large-scale group decision-making, in this study, after reviewing the existing approaches, a new model is presented using a statistical approach along with complex network analysis techniques. The opinions are generalized first, and then the network of opinions is built. This new method reduces the dimensions of the problem by considering a hierarchy of opinions. Different scenarios were designed for the evaluation. The results show that the effect of this generalization on dimension reduction depends on the parameters of the problem. We have shown that in a group decision scenario with 3000 decision-makers and 6 alternatives, 99% of the data was reduced. As dimension reduction is the main focus of the current research, the effect of consistency on the diversity of opinions has also been investigated, and the results show that opinion consistency affects opinion generalization, which in turn affects dimension reduction. In addition, in the performed simulations, three types of functions were used to calculate similarity. The aim was to determine the best similarity function for the decision problems whose purpose was to rank the available alternatives. The results show that Euclidean similarity is a strict criterion compared with Cosine similarity.

Managing Strategic Manipulation Behaviors Based on Historical Data of Preferences and Trust Relationships in Large-Scale Group Decision-Making

Managing Strategic Manipulation Behaviors Based on Historical Data of Preferences and Trust Relationships in Large-Scale Group Decision-Making

Robust maximum expert consensus model with adjustment path under uncertain environment

The maximum expert consensus model (MECM) is a commonly used consensus model in group decision making (GDM). In traditional MECM, the consensus constraints are not fully considered and the adjustment cost of decision maker (DM) is certain. Moreover, directing the DM’s opinion in a visual path is seldom considered in the consensus reaching process (CRP) of MECM. Inspired by these issues, this paper first proposed two MECMs with different types of consensus constraints. Then, this paper incorporated an adjustment path into MECM by using feedback coefficients that can prevent opinions from being overadjusted. Furthermore, the robust MECM (RMECM) is developed to address the uncertainty of unit adjustment cost under three uncertainty sets. Finally, the feasibility of the proposed models is verified by applying them to the allocation of special funds in the Chinese film industry, which is a large-scale group decision making (LSGDM) problem. The sensitivity analysis and comparative analysis are also conducted to show the efficiency of the proposed models.