Large-scale Group Decision Research Articles

A large scale group decision making approach in healthcare service based on sub-group weighting model and hesitant fuzzy linguistic information

Globally growing demand for healthcare has highlighted increasing requirements for healthcare management. Healthcare management is complex, and multi-faceted, with many stakeholders, all of whose opinions require consideration. Multi-criteria group decision making is thus necessary for effective healthcare decision making. The aim of this paper is to develop a large-scale group decision making (LSGDM) approach for healthcare management decision-making. Hesitant fuzzy linguistic term sets (HFLTSs) are used to describe the decision information. A clustering method based on the ideal points is proposed to cluster the decision makers (DMs) into several sub-groups. Then DMs’ preferences are fused by possibility distributed extended HFLTSs (PDEHFLTSs) so as to retain as much as decision information as possible. Based on the sub-group size and the proposed hesitant entropy of PDEHFLTSs, a sub-group weighting model is developed to derive the ranking with multiples and interval forms of the sub-group weights. The final weights of sub-groups are then determined by an optimization model which is derived by calculating the shortest distance from the PDEHFLTS positive ideal solution and the farthest distance from the PDEHFLTS negative ideal solution. An example for healthcare management is presented to illustrate the validity of the proposed model.

Optimal group selection model for large-scale group decision making

Research on large-scale group decision making (LSGDM) has generally focused on clustering and reaching a consensus. Often, participants (or decision makers) are unwilling to share their consensus or revise their preferences, which leads to the loss of time and money and even reduces the efficiency of decision making. Accordingly, in LSGDM, an optimal group derived from a large number of participants could improve the decision-making efficiency and reduce time consumption. In this paper, we investigate a model to select the optimal group for LSGDM with multiplicative preference relations. Notably, for individual and group preference relations, we define an individual logarithmic square compatibility measure and a group logarithmic square compatibility measure, respectively. Some properties associated with the individual logarithmic square compatibility measure and group logarithmic square compatibility measure are addressed. For a large number of preferences in LSGDM, the redundant preferences and optimal group are distinguished based on an optimal group selection model using the optimal participant weights obtained by minimizing the group logarithmic square compatibility model. Then, we demonstrate that the model is a convex quadratic programming problem. Moreover, the conditions for the existence of an optimal solution and the conditions for redundant preference relations in the group logarithmic square compatibility model are provided. In this context, the proposed model is verified to effectively identify the optimal group.

A Dynamic Adaptive Subgroup-to-Subgroup Compatibility-Based Conflict Detection and Resolution Model for Multicriteria Large-Scale Group Decision Making.

The current societal demands and technological developments have resulted in the participation of a large number of experts in making decisions as a group. Conflicts are imminent in groups and conflict management is complex and necessary especially in a large group. However, there are few studies that quantitatively research the conflict detection and resolution in the large-group context, especially in the multicriteria large-group decision making (GDM) context. This article proposes a dynamic adaptive subgroup-to-subgroup conflict model to solve multicriteria large-scale GDM problems. A compatibility index is proposed based on two kinds of conflicts among experts: 1) cognitive conflict and 2) interest conflict. Then, the fuzzy c -means clustering algorithm is used to classify experts into several subgroups. A subgroup-to-subgroup conflict detection method and a weight-determination approach are developed based on the clustering results. Afterward, a conflict resolution model, which can dynamically generate feedback suggestion, is introduced. Finally, an illustrative example is provided to demonstrate the effectiveness and applicability of the proposed model.

Social network community analysis based large-scale group decision making approach with incomplete fuzzy preference relations

Large-scale group decision making (LSGDM) is characterized by a large number of participators, multiple groups and a mass of decision data provided by the participators. With the development of social media and e-democracy technologies, the social relationships among group decision makers should be taken into consideration when we address LSGDM problems with incomplete fuzzy preference relations. In this paper, a social network community detection approach of social networks based on the fuzzy clustering method is proposed. Then, a method of repairing incomplete fuzzy preference relations based on the divided communities is proposed. Moreover, we have proposed a two-stage consensus reaching method to balance the number of iterations and modification range of original decision information, in which the group closeness centrality of community is applied to measure its importance. Also, the procedure for LSGDM with fuzzy preference relations based on social network community analysis is established. The feasibility and advantages of the proposed method for LSGDM based on social network community analysis are illustrated by an illustrative example of a flexible manufacturing system.

Sensor fusion based on Dempster‐Shafer theory of evidence using a large scale group decision making approach

Sensor fusion based on Dempster‐Shafer theory of evidence using a large scale group decision making approach

An objective and interactive‐information‐based feedback mechanism for the consensus‐reaching process considering a non‐support degree for minority opinions

AbstractThe consensus‐reaching process (CRP) to achieve higher unanimity and ensure common agreement before deriving a final decision has become an important procedure in group decision‐making problems. The demand for high‐quality decision results has motivated the development of large‐scale group decision‐making (LGDM). In such cases, the issue of minority opinion has gained awareness due to the related effects on enhancing consensus and decision quality. A minority opinion cannot exert an effect unless the majority attach importance to whether that opinion is supported or not. To reflect the effect of minority opinions on consensus, this paper establishes a LGDM framework with an objective and interactive‐information‐based feedback mechanism for the CRP. Given the natural forms of human expression, multi‐granular linguistic information and a 2‐tuple linguistic model are used. First, initial weights are objectively assigned to decision‐makers (DMs) to weaken the impact of the majority. Subsequently, a non‐support degree function is newly defined to reflect the extent to which other DMs dissent from a minority opinion. More importantly, feedback rules are constructed to make corresponding adjustments to the powers of discourse among all DMs in the attempt to reach consensus. Finally, the proposed three‐phase LGDM framework is applied to new product development (NPD), and simulation experiments are conducted based on two algorithms to verify the framework's applicability and feasibility.

Bid Evaluation for Major Construction Projects Under Large-Scale Group Decision-Making Environment and Characterized Expertise Levels

Bid Evaluation for Major Construction Projects Under Large-Scale Group Decision-Making Environment and Characterized Expertise Levels

Managing Multigranular Unbalanced Hesitant Fuzzy Linguistic Information in Multiattribute Large-Scale Group Decision Making: A Linguistic Distribution-Based Approach

With the increase of technological and societal demands, more and more decision makers are involved in the process of group decision making, which is called large-scale group decision making (LGDM). For an LGDM problem with linguistic information, it is common that different decision makers tend to provide linguistic assessments defined on multigranular linguistic term sets due to the difference in knowledge and culture background, and that hesitant fuzzy linguistic term sets (HFLTSs) are used by decision makers to model the hesitancy of their assessments. This article proposes first an algorithm to represent a linguistic distribution assessment (LDA) using a hesitant linguistic distribution (HLD). Two other algorithms are then proposed to transform an unbalanced HFLTS into a balanced LDA and to transform a balanced LDA into an unbalanced LDA, respectively. An approach is then proposed to deal with multiattribute LGDM problems with multigranular unbalanced hesitant fuzzy linguistic information based on these algorithms. In the proposed approach, all unbalanced hesitant fuzzy linguistic information is transformed into LDAs defined on a balanced linguistic term set, and then an LDA-based clustering algorithm is devised to cluster decision makers. Based on the clustering result, decision makers’ linguistic distribution decision matrices are further fused to obtain collective assessments of alternatives. In order to provide easy-to-understand linguistic results for decision makers, all LDAs of alternatives are represented by HLDs defined on each decision maker's initial linguistic term set. Finally, an example for the selection of subway lines is used to demonstrate the proposed approach.

A two-stage dynamic influence model-achieving decision-making consensus within large scale groups operating with incomplete information

The decision making environment has been dramatically affected by rapid developments in society and the economy. Large-scale group decision making (LSGDM) based on social network has become a vital research topic in the field of decision making science. In this paper, we propose a novel framework based on social network to manage the consensus reaching process (CRP) for LSGDM faced with incomplete information. In this framework, the large-scale group is first classified into several smaller sub-groups using a sub-group detection algorithm, based on the social network. Then, we propose an estimating method based on a collaborative filtering algorithm for estimating the missing preference information of the opinion leaders in each sub-group. The two-stage dynamic influence model for handling the consensus reaching process in LSGDM begins when the LSGDM is transformed into several smaller sub-group decision processes. In the first stage, a consensus model, based on opinion evolution, is proposed for the CRP within each sub-group. In the second stage, we consider each sub-group as a decision making unit. By focusing on the consensus problem across the sub-groups, we develop a novel opinion-leaders feedback strategy in order to help the sub-groups revise their opinions, working toward consensus. We provide an example of an application of our process to illustrate the validity of the proposed model for managing the CRP in LSGDM.

Adaptive consensus reaching process with hybrid strategies for large-scale group decision making

Large-scale group decision making, which involves dozens to hundreds of experts, is attracting increasing attention and has become an important topic in the field of decision making. Because of the clustering process, a large-scale group decision making problem can be divided into two levels: inter sub-group and intra sub-group. In existing consensus models under the large-scale group decision making environment, the degree of consensus within the intra sub-group is not truly taken into account. To deal with this issue, this work develops an adaptive consensus model for the sub-groups composed of hybrid strategies, with or without a feedback mechanism, according to the different levels of inter and intra degrees of consensus. These different levels of consensus are divided into four scenarios (high–high, high–low, low–high, low–low), and different feedback suggestions are generated corresponding to different cases. This hybrid mechanism can reduce the cost of supervision for the moderator. The fuzzy c-means clustering algorithm is used to classify experts. A weight-determining method combining the degree of cohesion and the size of a sub-group is introduced. Finally, an illustrative example is offered to verify the practicability of the proposed model. Some discussions and comparisons are provided to reveal the advantages and features of the proposed model.

A Feedback Mechanism With Bounded Confidence- Based Optimization Approach for Consensus Reaching in Multiple Attribute Large-Scale Group Decision-Making

Different feedback mechanisms have been developed in large-scale group decision-making (GDM) to provide the decision-makers with advices for preference adjustment with the aim of improving the group consensus level. However, the willingness of the decision-makers to accept these advices is rarely considered in the extant feedback mechanisms. In the field of opinion dynamics, this issue is studied by the bounded confidence model, which shows that the decision-makers only consider the preferences that differ from their own preferences not more than a certain confidence level. Following this idea, this article proposes a large-scale consensus model with a bounded confidence-based feedback mechanism to promote the consensus level among decision-makers with bounded confidences. Specifically, this feedback mechanism classifies the decision-makers into different clusters and provides the corresponding clusters with more acceptable advices based on a bounded confidence-based optimization approach. Finally, through the numerical example and the simulation analysis, the use of the model is introduced, and the effectiveness of the model is justified.

Social Recommendation With Large-Scale Group Decision-Making for Cyber-Enabled Online Service

Along with the development of several emerging computing paradigms and information communication technologies, it is said that cyber computing technology is playing an increasingly important role across cyber-related systems and applications. In this article, we focus on cyber–social computing and propose a computational model that integrates large-scale group decision-making (LSGDM) into social recommendations for cyber-enabled online services. As a concrete application example, a graph model is built to describe the LSGDM problem among researchers in scholarly big data environments. Following the basic profiling to describe decision-makers within scholarly networks, measures are defined to evaluate one researcher’s academic performance and research outcome and further quantify correlations between them based on their collaboration relationships in a constructed network model. A two-stage large-scale decision-making solution is then proposed for social recommendations: A network partition algorithm is developed based on the identification of experts along with their influence extending to a group of researchers, and a random walk with the restart-based algorithm is improved to calculate the weighted decisions for group decision aggregation and alternative ranking. Experiments using the real-world data demonstrate the usefulness and effectiveness of our proposed model and method, which can provide the target researcher with more reliable recommendations.

Managing minority opinions in micro-grid planning by a social network analysis-based large scale group decision making method with hesitant fuzzy linguistic information

The growth of global electricity demand has put forward higher requirements for power distribution networks. The high cost of the large-scale power system and the voice for the use of renewable energy impel the birth of the micro-grid which plays a complementary role in the power generation of large-scale power system. The construction of micro-grid planning is complex and many stakeholders’ opinions should be considered for a comprehensive evaluation. Furthermore, the development of social big data techniques, such as e-marketplace and e-democracy, makes experts have social relationships among them. This study aims to develop a consensus model to manage minority opinions for large-scale group decision making with social network analysis for micro-grid planning. To deal with the vague and uncertain features in complex micro-grid planning problems, experts are supposed to use hesitant fuzzy linguistic term sets to express their opinions. A social network analysis-based clustering method is introduced to classify experts. Besides, in a large-scale group decision making problem, the opinions of experts should be fully considered, especially the minority opinions. This model considers the minority opinions in a micro-grid planning problem and provides an approach to manage these opinions. Finally, we use an illustrative example concerning the micro-grid planning decision making in Ali district in Tibet to demonstrate the effectiveness and practicability of the proposed model.

Social network analysis-based consensus-supporting framework for large-scale group decision-making with incomplete interval type-2 fuzzy information

Experts are assumed to be mutually independent and incomplete decision matrices are used, in traditional large-scale group decision-making (LSGDM). Social network analysis (SNA) methods are useful for managing LSGDM problems considering the social relationships among experts. This study develops a novel SNA-based decision framework for addressing LSGDM problems with incomplete interval type-2 fuzzy information. First, a community detection algorithm is designed to classify experts, and a trust propagation and aggregation mechanism is developed to obtained experts’ indirect trust values and weights based on the social trust network. Second, a multi-objective programming model integrated with trust relationships among experts is constructed to estimate the missing values in incomplete interval type-2 fuzzy decision matrices. The proposed estimation approach is robust and effectively estimates missing values while maintaining high discrimination levels. Subsequently, an SNA-based consensus-reaching model is designed for achieving a group consensus based on a twofold feedback adjustment mechanism, namely, adaptive judgment and weight feedback. Finally, an illustrative example is presented to demonstrate the applicability of the proposed approach. A simulation and comparison study is conducted to verify the efficiency and advantages of the SNA-based consensus-supporting framework for LSGDM.

Managing personalized individual semantics and consensus in linguistic distribution large-scale group decision making

A linguistic distribution assessment is an effective approach to represent uncertain preferences in large-scale group decision making (LSGDM). The same word often signifies different things for different decision makers in linguistic distribution assessments, which is called personalized individual semantics (PIS). Moreover, preference conflicts widely exist in LSGDM. This paper develops a new framework to address PIS and consensus in LSGDM using linguistic distribution preference relations (LDPRs). In the proposed LSGDM framework, a consistency-driven optimization model is put forward to produce the numerical scales with the PIS by maximizing the consistency of the additive preference relation that is transformed from its LDPR. Then, a preference clustering technique is employed to decompose decision makers into different clusters for managing their preferences. Next, the paper devises a two-stage-based consensus reaching model to manage the individual consistency and group consensus, which seeks to minimize the preference information loss. The first stage aims to assist decision makers in achieving a consensus within each obtained cluster, and the second stage is devoted to facilitating the consensus building among the different clusters. Finally, a case study that evaluates water management plans and a comparative analysis with the existing baseline approach are conducted to assess the feasibility and validity of the proposed LSGDM framework.

Ordinal consensus measure with objective threshold for heterogeneous large-scale group decision making

Because of the increasing complexity of real-world decision-making environment, there is a trend that a large number of decision-makers are becoming involved in group decision making problems. In large-scale group decision making problems, owing to various backgrounds and psychological cognition, it is natural to use heterogeneous representation forms (quantitative or qualitative) to express distinct preference information for different decision-makers. In this paper, we investigate the consensus reaching process in the environment of heterogeneous large-scale group decision making. A novel ordinal consensus measure with an objective threshold based on preference orderings is proposed. This process contains five parts: (1) obtaining ordinal preferences; (2) classifying all decision-makers into several subgroups using the ordinal k-means clustering algorithm; (3) measuring consensus levels of subgroups and the global group using novel ordinal consensus indexes; (4) providing suggestions for decision-makers to revise preferences using feedback strategies; (5) obtaining final decision result. An illustrative example is provided to verify the implementation of the proposed consensus model. Lastly, we discuss the approaches to determine the appropriate number of clusters and the initialization center points for the proposed ordinal k-means clustering algorithm.

Large-scale group decision making with multiple stakeholders based on probabilistic linguistic preference relation

A large number of stakeholders take part in the process of decision making, namely, large-scale group decision making (LGDM) problems. Every stakeholder utilises a linguistic preference relation (LPR) to represent her/his preference information for alternatives. Then, a probabilistic LPR (PLPR) is established to represent the group preference. However, some stakeholders may only provide partial preference information about the alternatives. Thus, a PLPR with incomplete probabilities can be used to manage LGDM problems in complex environments. Based on the defined expected multiplicative consistency of PLPR, a probability computation model is established by mathematical programming to derive the missing probabilities of PLPR. In addition, an iterative algorithm to improve the consistency is proposed to obtain the PLPR with satisfactory consistency. Finally, a real-world investment decision-making problem with multiple stakeholders is solved to demonstrate the effectiveness of the proposed method.

Linguistic Distribution-Based Optimization Approach for Large-Scale GDM With Comparative Linguistic Information: An Application on the Selection of Wastewater Disinfection Technology

Managing comparative linguistic expressions (CLEs) information is a key issue in group decision-making (GDM). A transformation approach has been previously defined to convert CLEs into hesitant fuzzy linguistic terms sets (HFLTSs). However, it is noted that the occurring possibilities of the linguistic terms in the HFLTSs are assumed equal. This assumption might sometimes not capture the real opinions of the decision makers. Linguistic distribution assessments (LDAs) are an effective way to deal with this issue. This paper develops a linguistic distribution-based optimization approach for converting CLEs into LDAs, in which we assume that decision makers provide their opinions using preference relations with CLEs. Particularly, the proposed optimization approach is based on the use of a consistency-driven methodology, which seeks to minimize the inconsistency level of LDA preference relations obtained by transforming the original CLE preference relations elicited from decision makers. The linguistic distribution-based optimization approach is further developed to transform CLEs into interval LDAs to increase their flexibility. Moreover, society and technology trends make it possible to involve and manage large groups of decision makers in GDM environment. Therefore, a large-scale GDM framework with CLE information is designed based on the linguistic distribution-based optimization approach. To justify the effectiveness and applicability of the proposed methodology, it is applied to solve a real large-scale GDM problem, pertaining the selection of the best sustainable disinfection technique for wastewater reuse projects. A comparison against a baseline method is likewise provided to highlight the advantages and innovations of our proposal.

Consensus model for large-scale group decision making based on fuzzy preference relation with self-confidence: Detecting and managing overconfidence behaviors

With the development of intelligent decision-making, one kind of decision modes involves a large number of decision-makers (DMs), which is called large scale group decision making (LSGDM). In LSGDM, overconfidence is one of the common behaviors because of many DMs’ participation and the bounded rationality of human decision. Overconfidence usually has a negative impact on LSGDM and can even lead to failure in the final decision(s). To achieve consensus is very important for LSGDM. Different consensus models of LSGDM have been proposed, while the DMs’ overconfidence behaviors in the consensus have not been concerned. Hence, the purpose of this paper is to propose a consensus model which considers overconfidence behaviors, and the paper mainly focuses on LSGDM based on fuzzy preference relations with self-confidence (FPRs-SC). In the proposed model, a DM clustering method, which combines fuzzy preference values similarity and self-confidence similarity, is used to classify the DMs with similar opinions into a subgroup. A group consensus index which considers both the fuzzy preference values and self-confidence is presented to measure the consensus level among DMs. An overconfidence measurement is given to detect the DMs’ overconfidence behaviors in the consensus. Subsequently, the detailed overconfidence behavior analysis is presented involving two aspects: fuzzy preference values consensus and self-confidence consensus. A dynamic weight punishment mechanism is implemented for overconfident DMs to improve the consensus efficiently. The effectiveness and advantages of the presented consensus model are validated by a numerical example and comparative analysis.

A clustering method for large-scale group decision-making with multi-stage hesitant fuzzy linguistic terms

A large-scale group decision-making (LGDM) problem is studied from the perspective of multi-stage hesitant fuzzy linguistic term sets (MHFLTSs). Solving an LGDM problem requires two processes: a clustering process is helpful for breaking down the larger problem into smaller pieces for simplification purposes, and a selection process is utilized for obtaining a final solution. Along with the MHFLTSs, the decision-making process should include consideration of the distinctiveness of the term sets. With this idea in mind, we propose a clustering method and a selection method based on consideration of multiple reference points derived from the MHFLTSs. First, taking into account the HFLTS characteristics, an expert similarity measurement is proposed based on both the expectation distance and hesitancy similarity between two HFLTSs. Second, multiple reference points are examined: a positive ideal reference point and a development reference point. Reference similarity is then measured by combining these two points. With the goal of maximizing the reference similarity of the alternatives, the third element is the establishment of an optimization model for determining the stage and attribute weights based on consideration of the sensitivity of these two weights to the problem. In the fourth step, a fuzzy clustering method is applied in order to create expert clusters based on expert similarity as well as on the stage and attribute weights. An additional feature is a selection analysis based on the reference similarity between the cluster centers, which entails consideration of the degree of certainty of each cluster as a measure of cluster weighting. The final phase of the research is the application of the proposed method to two cases as a means of illustrating the applicability and feasibility of the new method.