Large-scale Group Decision Research Articles

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.

Consensus reaching process in large-scale group decision making based on bounded confidence and social network

Consensus reaching process (CRP) is a dynamic and interactive method used to reach a group decision. Now that social networks and mobile internet are prominent features in daily life, more experts are able to take participate in decision making in the network and their opinions are influenced by others in the decision-making process. Therefore, how to use the difference of opinions and the relationships between experts to promote the consensus is an important issue. This paper proposed a CRP approach for large-scale group decision-making based on bounded confidence and social network to manage experts’ opinions. Firstly, a fast unfolding algorithm was used to reduce the dimension of the large-scale and the experts’ weights were obtained by social network analysis. Secondly, the CRP was built based on the Manhattan distance, and the feedback mechanism was developed to adjust experts’ opinions based on bounded confidence and social network when the experts did not reach a consensus. A numerical example was used to show the feasibility of the proposed approach and the results illustrated that the consensus speed was faster and the information managed more efficiently. The comparisons with other approaches showed the advantage of our proposed approach. Finally, simulation experiments were given to verify the effectiveness of our proposed approach.

A Clustering Method with Historical Data to Support Large-Scale Consensus-Reaching Process in Group Decision-Making

With the rapid development of information technology and social network, the large-scale group decision-making (LSGDM) has become more and more popular due to the fact that large numbers of stakeholders are involved in different decision problems. To support the large-scale consensus-reaching process (LCRP), this paper proposes a LCRP framework based on a clustering method with the historical preference data of all decision makers (DMs). There are three parts in the proposed framework: the clustering process, the consensus process and the selection process. In the clustering process, we make use of an extended k-means clustering technique to divide the DMs into several clusters based on their historical preferences data. Next, the consensus process consists of the consensus measure and the feedback adjustment. The consensus measure aims to calculate the consensus level among DMs based on the obtained clusters. If the consensus level fails to reach the pre-defined consensus threshold, it is necessary to make the feedback adjustment to modify DMs' preferences. At last, the selection process is carried out to obtain a collective ranking of all alternatives. An illustrative example and detailed simulation experiments are demonstrated to show the validity of the proposed framework against the traditional LCRP models which just consider the preference information of DMs at only one stage for clustering.

A large-scale group decision making method to select the ideal mobile health application for the hospital.

Mobile health, which is not limited by time and space, can effectively alleviate the imbalance of medical resources. Currently, more and more hospitals begin to pay attention to online medical care and actively expand their mobile channels. Among of which, the cooperation with the third-party platform is an effective way to expand the online services of most hospitals. With the increasing number of mobile health applications (mHealth apps), it is difficult to select the ideal application. Most of the existing studies on mHealth app selection are conducted from the perspective of users who have health needs, which is insufficient. The views of multiple stakeholders should be taken into account. mHealth app selection can be regarded as a large-scale group decision making (LSGDM) problem. In this paper, a hybrid LSGDM method is proposed to select the mHealth app with the highest user satisfaction. First, the weights of criteria are obtained based on quality function deployment and 2-additive measure. Furthermore, a consensus model that considers cooperative and non-cooperative behaviors of decision makers is applied to select the ideal mHealth app. Finally, an illustrative example is implemented to exhibit the utility and validity of the proposed model.

A novel similarity-based consensus model for probabilistic linguistic sets and its application in multi-attribute large-scale group decision making

A novel similarity-based consensus model for probabilistic linguistic sets and its application in multi-attribute large-scale group decision making

Clustering and compatibility-based approach for large-scale group decision making with hesitant fuzzy linguistic preference relations: An application in e-waste recycling

Considering that large-scale group decision making (LSGDM) has been widely concentrated on clustering and lower compatibility problems, this paper focuses on clustering and consensus reaching of hesitant fuzzy linguistic preference relations (HFLPRs) in LSGDM. Firstly, we define the hesitant degree and the fuzzy degree functions for hesitant fuzzy linguistic term sets (HFLTSs) according to their scale and deviation of elements. To normalize HFLTSs with different length, a minimum deviation model based on the hesitant and the fuzzy degrees is developed. After that, we propose a new similarity measure and compatibility measure via integrating the hesitant and fuzzy degrees. Then a clustering process combining similarity and compatibility is used to assign large-scale experts into several clusters. The clustering method not only strengths the cohesion of subgroups, but also guarantees that HFLPRs in subgroups are acceptable compatibility. Moreover, a compatibility adjusting process with self-selected feedback mechanism is presented to improve the compatibility level of clusters until they reach a predefined threshold in selection process. And we combine the number of members in subgroups and the compatibility level of subgroups to determine weights of subgroups. Finally, a case of e-waste recycling and comparison are analyzed to show the availability of the proposed method.

Consensus Building for Uncertain Large-Scale Group Decision-Making Based on the Clustering Algorithm and Robust Discrete Optimization

Consensus Building for Uncertain Large-Scale Group Decision-Making Based on the Clustering Algorithm and Robust Discrete Optimization

A survey of group decision making methods in Healthcare Industry 4.0: bibliometrics, applications, and directions.

Healthcare Industry 4.0 refers to intelligent operation processes in the medical industry. With the development of information technology, large-scale group decision making (GDM), which allows a larger number of decision makers (DMs) from different places or sectors to participate in decision making, has been rapidly developed and applied in Healthcare Industry 4.0 to help to make decisions efficiently and smartly. To make full use of GDM methods to promote the developments of the medical industry, it is necessary to review the existing relevant achievements. Therefore, this paper conducts an overview to generate a comprehensive understanding of GDM in Healthcare Industry 4.0 and to identify future development directions. Bibliometric analyses are conducted in order to learn the development trends from published papers. The implementations of GDM methods in Healthcare Industry 4.0 are reviewed in accordance with the paradigm of the general GDM process, which includes information representation, dimension reduction, consensus reaching, and result elicitation. We also provide current research challenges and future directions regarding medical GDM. It is hoped that our study will be helpful for researchers in the field of GDM in Healthcare Industry 4.0.

Granular computing and optimization model-based method for large-scale group decision-making and its application

In large-scale group decision-making process, some decision makers hesitate among several linguistic terms and cannot compare some alternatives, so they often express evaluation information with incomplete hesitant fuzzy linguistic preference relations. How to obtain suitable large-scale group decision-making results from incomplete preference information is an important and interesting issue to concern about. After analyzing the existing researches, we find that: i) the premise that complete preference relation is perfectly consistent is too strict, ii) deleting all incomplete linguistic preference relations that cannot be fully completed will lose valid assessment information, iii) semantics given by decision makers are greatly possible to be changed during the consistency improving process. In order to solve these issues, this work proposes a novel method based on Granular computing and optimization model for large-scale group decision-making, considering the original consistency of incomplete hesitant fuzzy linguistic preference relation and improving its consistency without changing semantics during the completion process. An illustrative example and simulation experiments demonstrate the rationality and advantages of the proposed method: i) semantics are not changed during the consistency improving process, ii) completion process does not significantly alter the inherent quality of information, iii) complete preference relations are globally consistent, iv) final large-scale group decision-making result is acquired by fusing complete preference relations with different weights.

An Influence Network-Based Consensus Model for Large-Scale Group Decision Making with Linguistic Information

The vast majority of the existing social network-based group decision-making models require extra information such as trust/distrust, influence and so on. However, in practical decision-making process, it is difficult to get additional information apart from opinions of decision makers. For large-scale group decision making (LSGDM) problem in which decision makers articulate their preferences in the form of comparative linguistic expressions, this paper proposes a consensus model based on an influence network which is inferred directly from preference information. First, a modified agglomerative hierarchical clustering algorithm is developed to detect subgroups in LSGDM problem with flexible linguistic information. Meanwhile, a measure method of group consensus level is proposed and the optimal clustering level can be determined. Second, according to the preference information of group members, influence network is constructed by determining intra-cluster and inter-cluster influence relationships. Third, a two-stage feedback mechanism guided by influence network is established for the consensus reaching process, which adopts cluster adjustment strategy and individual adjustment strategy depending on the different levels of group consensus. The proposed mechanism can not only effectively improve the efficiency of consensus reaching of LSGDM, but also take individual preference adjustment into account. Finally, the feasibility and effectiveness of the proposed method are verified by the case of intelligent environmental protection project location decision.

A Large‐Scale Group Decision‐Making Consensus Model considering the Experts’ Adjustment Willingness Based on the Interactive Weights’ Determination

This study proposes a large‐scale group decision‐making (LSGDM) consensus model considering the experts’ adjustment willingness based on the interactive weights’ determination, which can be applied to an LSGDM problem through a case of earthquake shelters. The main contributions of our research are of three aspects as follows. First, the determination method of the interactive weight, which obtains the DMs’ attitude towards the decision‐making results, is presented. The subgroups’ weights are calculated, and the unit adjustment cost for each DM is defined. Second, we introduce an objective consensus threshold by the mean and variance of the consensus level for each subgroup. Subsequently, an identification rule is performed to determine the DM to be adjusted with the large difference and the low adjustment cost. And we developed a novel consensus adjustment method that takes the DMs’ adjustment willingness into account to retain as much original information as possible. Thirdly, in order to reduce the subjectivity of the preset consensus threshold and the maximum number of iterations, an objective consensus termination condition that combines the current group consensus level and the consensus adjustment rate is put forward. Finally, the proposed model has demonstrated its effectiveness and superiority based on the comparative and sensitive analysis through a practical example.

An Approach to Prevent Weight Manipulation by Minimum Adjustment and Maximum Entropy Method in Large-Scale Group Decision Making

An Approach to Prevent Weight Manipulation by Minimum Adjustment and Maximum Entropy Method in Large-Scale Group Decision Making

An Approach for Reaching Consensus in Large-Scale Group Decision Makings in Social Networks

An Approach for Reaching Consensus in Large-Scale Group Decision Makings in Social Networks

A Hierarchical Stackelberg Game Approach for Large-Scale Group Decision Making Based on Cooperative Incentives

A Hierarchical Stackelberg Game Approach for Large-Scale Group Decision Making Based on Cooperative Incentives

Consensus reaching process in large-scale group decision making based on opinion leaders

With the development of science and technology, large-scale group decision making based on social network has become a crucial issue, and opinion leaders play an increasingly important role in the process of opinion dissemination and information transmission by virtue of their own influence, even affect the direction of decision-making. This paper proposes a framework of consensus reaching process for large-scale groups based on opinion leaders. In this framework, we first use the fast unfolding algorithm to divide the large-scale group into several small sub-clusters, and identify the opinion leaders of each sub-cluster. Then, we aggregate the opinions of the opinion leaders in each sub-cluster and construct the group decision matrix with the opinions of each sub-cluster as a new whole. A feedback mechanism based on opinion leaders is developed to help the opinion leaders who do not reach a consensus change their opinions to promote consensus. Finally, a numerical example about emergency shelter selection is given to illustrate the effectiveness of the proposed approach.

Social network clustering and consensus-based distrust behaviors management for large-scale group decision-making with incomplete hesitant fuzzy preference relations

With the development of social network platforms, large-scale group decision-making in social network (LSGDM-SN) has been formed. As decision makers (DMs) come from different fields and have complex individual backgrounds, which leads to their distrust in the moderator. Moreover, in LSGDM-SN, since DMs can hardly grasp all the information about the decision problem, the hesitant fuzzy preference relations (HFPRs) they have expressed may be incomplete. However, in current LSGDM-SN issues, the distrust behaviors and incomplete HFPRs have never been discussed simultaneously. In this context, this paper aims to propose a method to estimate incomplete values in HFPRs, and develop a consensus management process which considers distrust behaviors. This paper focuses on LSGDM-SN on the basis of social network clustering and consensus-based distrust behaviors management with incomplete HFPRs. In this paper, a social network clustering method based on grey clustering algorithm is proposed to classify the DMs with similar social clustering degree into a subset. Afterwards, a method including two situations is developed to estimate incomplete values in HFPRs. Furthermore, an identification mechanism is presented to detect the DMs’ distrust behaviors, and three modification strategies are provided for managing different types of distrust behaviors. In addition, a case study is given to illustrate the feasibility of the proposed method. Finally, comparative analysis and discussion are explored to verify the advantages of the proposed LSGDM-SN with incomplete HFPRs.

Personalized travel recommendation: a hybrid method with collaborative filtering and social network analysis

ABSTRACT This study proposes a hybrid method for producing personalized travel recommendation that better meet travellers’individual needs and also improve their online booking experience. The proposed method integrates multi-attribute collaborative filtering with social network analysis within the framework of large-scale group decision-making. It includes four modules, i.e. identification of online opinion experts, construction of a social network, detection of user communities, and interactively produced of personalized travel recommendation. Specifically, the preliminary user filtering and k-means clustering approach are utilized to identify the online opinion experts for a specific travel recommendation issue. Then, social network construction and its community detection process are adopted to alleviate the sparsity problem. Finally, the travel alternatives are ranked to select recommendations, and this is done interactively with travellers to handle the cold start problem. With the proposed method, a better online booking experience can be achieved for travellers, as they are presented with a more appropriate set of recommended options and so can make better travel decisions.

Big data-driven fuzzy large-scale group decision making (LSGDM) in circular economy environment

With the continuous development of big data techniques, more and more group decision-making problems involve multiple decision-makers. In the era of big data, massive data can improve decision-making ability and provide good support for decision-making. Large-Scale Group Decision Making (LSGDM) has been developed, and it discusses many cases where decision-makers are related to the decision-making process. However, the current LSGDM model has some shortcomings. To end this issue, this paper proposed a novel LSGDM method to study the role of the big data-driven decision-making issue. In this regard, existing literature and expert interviews were used to collect research standards. Then, a fuzzy LSGDM judgement matrix decision-making method based on group analysis is proposed. Finally, the method is applied to evaluate the takeout service platform. The results show that seven respondents need to adjust the acceptable consistency of the fuzzy judgement matrix, and they can quickly reach a satisfactory consensus. The results can further enrich and improve the group decision-making method based on a fuzzy judgement matrix.

Personalized individual semantics based consensus reaching process for large-scale group decision making with probabilistic linguistic preference relations and application to COVID-19 surveillance

This paper develops a new personalized individual semantic (PIS) based consensus reaching process (CRP) for large-scale group decision making (LSGDM) with probabilistic linguistic preference relations (PLPRs) and applies to the selection of COVID-19 surveillance plans. Firstly, considering that a linguistic term means different things to different decision makers (DMs), a new definition of distance between probabilistic linguistic term sets (PLTSs) is defined considering the PIS of a DM. Then, a consistency-driven optimization model is built to determine the PIS of linguistic terms in a PLPR of a DM by maximizing the consistency of a PLPR. DMs’ weights can be acquired through a programming model by minimizing the distance between the individual semantic and the collective semantic. Besides, a clustering algorithm based on PIS is devised for dividing DMs into several subgroups with similar semantic. Based on the obtained subgroups, the corresponding moderators can be identified by DMs with unacceptable consensus level (CL). Several minimum preference adjustment models are constructed to obtain the adjusted PLPRs. These models not only can sufficiently consider the willingness of DMs to modify their preferences but also can improve their CLs in CRP. Finally, an illustration example of selection of college COVID-19 surveillance plans is offered to demonstrate the proposed method and to verify the effectiveness of the proposed model.

A comprehensive minimum cost consensus model for large scale group decision making for circular economy measurement

Since the first report on the Circular Economy (CE) appeared in 2013, there has been an explosion of interest in the subject by society and the business world. Thus, a base of academic literature has been developed, seeking the establishment of principles that serve as a theoretical foundation for the concept of CE. Governments demand to know how organizations are evolving in the transition towards the new production model. However, despite the efforts of researchers and companies to develop effective measurement systems, it is not easy to decide which aspects to measure, nor to determine the degree of intensity in which an organization implements the CE model. The measurement proposals combine different methodologies that are costly and time consuming procedures. We propose a comprehensive minimum cost consensus model for large scale group decision making, in which the initial experts’ preferences are automatically adjusted to obtain the measurement and cost of indicators, so that they might agree on the measurements implemented. The main aim of this research is not only to provide a quick, useful and correct method for measuring the CE, but also to show its correctness, advantages and usefulness by comparing its performance with a real case.