Large Group Decision Research Articles

Optimizing Emergency Shelter Selection in Earthquakes Using a Risk-Driven Large Group Decision-Making Support System

This study presents a novel risk-based decision support system for helping disaster risk management planners select the best locations for emergency shelters after an earthquake. The system starts by identifying 18 criteria, based on stakeholder analysis, that are important for selecting shelter sites. These criteria are then standardized to reflect their importance in the site selection process. Next, a Large Group Decision-Making (LGDM) model is used to determine the weight of each criterion based on collective intelligence. Finally, the Ordered Weighted Average (OWA) method is used to assess the suitability of different geographical locations for emergency shelters, resulting in a suitability map. The factors that were most significant for selecting the best emergency shelters were the distance from the fault, population density, access to green spaces, and building quality. The area of the optimal sites for emergency shelters in the region varied depending on the decision-maker’s risk attitude, ranging from 4% in an extremely pessimistic scenario to 28% in an extremely optimistic scenario. This system combines Geographic Information Systems (GIS) and LGDM to help decision-makers identify the optimal sites for emergency shelters under different risk levels, which can contribute to better-informed decision-making regarding disaster resilience.

A Combined Weighting Based Large Scale Group Decision Making Framework for MOOC Group Recommendation.

Massive open online courses (MOOC) are free learning courses based on online platforms for higher education, which not only promote the open sharing of learning resources, but also lead to serious information overload. However, there are many courses on MOOCs, and it can be difficult for users to choose courses that match their individual or group preferences. Therefore, a combined weighting based large-scale group decision-making approach is proposed to implement MOOC group recommendations. First, based on the MOOC operation mode, we decompose the course content into three stages, namely pre-class, in-class, and post-class, and then the curriculum-arrangement-movement- performance evaluation framework is constructed. Second, the probabilistic linguistic criteria importance through intercriteria correlation method is employed to obtain the objective weighting of the criterion. Meanwhile, the word embedding model is utilized to vectorize online reviews, and the subjective weighting of the criteria are acquired by calculating the text similarity. The combined weighting then can be obtained by fusing the subjective and objective weighting. Based on this, the PL-MULTIMIIRA approach and Borda rule is employed to rank the alternatives for group recommendation, and an easy-to-use formula for group satisfaction is proposed to evaluate the effect of the proposed method. Furthermore, a case study is conducted to group recommendations for statistical MOOCs. Finally, the robustness and effectiveness of the proposed approach were verified through sensitivity analysis as well as comparative analysis.

A Novel GDMD-PROMETHEE Algorithm Based on the Maximizing Deviation Method and Social Media Data Mining for Large Group Decision Making

Multi-attribute group decision making is widely used in the real world, and many scholars have done a lot of research on it. The public’s focus on emergencies can provide an important reference for emergency handling decision making in the social media big data environment. Due to the complexity of emergency handling decision making, the asymmetry of user evaluation information is easy to cause the loss of important information. It is very important to mine valuable information for decision making through online reviews. Then, a generalized extended hybrid distance measure method between the probabilistic linguistic term sets is proposed. Based on this, an extended GDMD-PROMETHEE large-scale multi-attribute group decision-making method is proposed as well, which can be used to decision making under symmetric information and asymmetric information. Firstly, web crawler technology is used to explore the topics of public concern of emergency handling on social media platforms, and use k-means cluster analysis to classify the crawling variables, then the attributes and subjective weights of emergency handling plans are obtained by TF-IDF and Word2vec technology. Secondly, in order to better retain the linguistic evaluation information from decision-makers, a new generalized probabilistic hybrid distance measure method based on Hamming distance is proposed. Considering the difference of decision makers’ evaluation, the objective weight of decision makers is calculated by combining the maximum deviation method with the new extended hybrid Euclidean distance. On this basis, the comprehensive weights of the attributes are calculated by combining subjective and objective factors. Meanwhile, this paper realizes the distance measures and information fusion of probabilistic linguistic term sets under cumulative prospect theory, and the ranking results of the emergency handling plans based on the extended GDMD-PROMETHEE algorithm are given. Finally, the feasibility and effectiveness of the extended GDMD-PROMETHEE algorithm are verified by the case study of the explosion accident handling decision making of Shanghai “6.18” Petrochemical, and the comparative analyses between the several traditional algorithms demonstrate the extended GDMD-PROMETHEE algorithm is more scientific and superior in this paper.

A Large Group Decision Making Method Considering Experts’ Non-cooperative Behavior for Investment Selection of Renewable Energy Projects

The rapid expansion of renewable energy has attracted the attention of investors, which makes the evaluation of renewable energy projects a momentous issue. As the investment selection of renewable energy projects requires the joint discussion of experts from different professional backgrounds (such as energy, transportation, construction, economy, environment, etc.), it belongs to the category of large group decision-making (LGDM). Therefore, this paper is devoted to propose a novel LGDM method considering experts’ non-cooperative behavior for investment selection of renewable energy projects. First, considering that the complexity of renewable energy projects makes it difficult for experts to express their views in a single linguistic word, the hesitant fuzzy linguistic term set is used as the tool for expert evaluation in this paper. Second, since the assessment information provided by experts from different fields are often heterogeneous, a consensus-reaching process with a feedback mechanism is introduced which comprehensively considers three reliable sources: the experts’ trust relationship in the social trust network, the consensus contribution in the subgroup and the opinions’ similarity among experts. Further, to improve the efficiency and rationality of decision-making, an experts’ historical adjustment data-based non-cooperative behavior management method is proposed. Finally, the effectiveness and innovation of the proposed method are verified by a case of renewable energy power generation project investment selection in Qingdao, China and a series of comparative analysis.

A Large Group Emergency Decision-Making Method Based on Uncertain Linguistic Cloud Similarity Method

In recent years, the consensus-reaching process of large group decision making has attracted much attention in the research society, especially in emergency environment area. However, the decision information is always limited and inaccurate. The trust relationship among decision makers has been proven to exert important impacts on group consensus. In this study, we proposed a novel uncertain linguistic cloud similarity method based on trust update and the opinion interaction mechanism. Firstly, we transformed the linguistic preferences into clouds and used cloud similarity to divide large-scale decision makers into several groups. Secondly, an improved PageRank algorithm based on the trust relationship was developed to calculate the weights of decision makers. A combined weighting method considering the similarity and group size was also presented to calculate the weights of groups. Thirdly, a trust updating mechanism based on cloud similarity, consensus level, and cooperation willingness was developed to speed up the consensus-reaching process, and an opinion interaction mechanism was constructed to measure the consensus level of decision makers. Finally, a numerical experiment effectively illustrated the feasibility of the proposed method. The proposed method was proven to maximally retain the randomness and fuzziness of the decision information during a consensus-reaching process with fast convergent speed and good practicality.

A large scale group decision making system based on sentiment analysis cluster

Nowadays, group decision making is an everyday occurrence in different scenarios, such as marketing or social networks. These social networks have facilitated communication between experts because they do not need to meet in person. However, communication between experts through the internet generates three problems: the management of large amounts of information, the fact that experts often provide their information using natural language, and the lack of analysis of experts’ intentions. In this paper, we propose a novel large scale group decision making method to manage the information generated by a large number of experts, using the natural language processing approach, specifically sentiment analysis. This approach makes it possible to detect the degree of positivity and aggressiveness of each expert and thus proceed to a classification. Once the behaviours are detected, the experts are grouped according to them and, for each group, a weight and a unique preference relation is obtained. In addition, we propose an optimised consensus analysis process, in which it is not necessary to compare all experts with each other, but only groups of experts.

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

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

Consensus-trust Driven Bidirectional Feedback Mechanism for Improving Consensus in Social Network Large-group Decision Making

Consensus-trust Driven Bidirectional Feedback Mechanism for Improving Consensus in Social Network Large-group Decision Making

A non-cooperative behavior management method for multi-attribute large group decision-making

In multi-attribute large group decision-making (MALGDM), the ideal state indicates a high degree of consensus among a set of decision-makers (DMs). It is complex to reach consensus because the number of decision attributes and DMs increases. Thus, we developed a novel consensus model to manage the decision-making in large group based on the non-cooperative behavior. The improved clustering method takes account of the similarities among different DMs. Similar DMs will be grouped into the same group. The consensus threshold is determined from an objective and subjective aspect to judge whether the consensus reaching process continues. With the introduction of three non-cooperative behaviors, we investigated a non-cooperative behavior detection method under the change of consensus level. Base on the number of DMs who are willing to change their preliminary views and the change value of consensus level, the non-cooperative degree of subgroup can be computed. According to the non-cooperative degree, the subgroups’ weight can be modified to raise the consensus level. Meanwhile, the subgroup is allowed to change. Based on the adjustment amount of DMs’ opinions, whether decision maker (DM) belongs to this subgroup is recalculated. Finally, an emergency decision-making problem in flood disaster is applied to manifest the feasibility and distinctive features of the proposed method.

Reliability-based multi-attribute large group decision making under probabilistic linguistic environment

This study proposes a more rational and effective multi-attribute large group decision making (MALGDM) method with probabilistic linguistic term set (PLTS) from reliability perspective. A reliability measure method is first proposed to compute the reliability degree of the PLTS, and then a normalization method is presented to normalize the ignorant PLTSs with respect to maximizing their reliability degrees. An efficient clustering method combining the opinion similarity of experts and the reliability degrees of the clusters formed is introduced. Moreover, an objective method of determining the similarity and reliability thresholds is presented. After classifying the large-scale experts, the consensus levels of clusters and the global consensus level are measured and the cluster that need to adjust information is identified based on its consensus level and reliability degree. Then, an optimization model to maximize the global consensus level and the global reliability degree is then built to obtain the evaluation values for improving the consensus levels and reliability degrees. The deviation between the expectation values of the evaluation values before and after adjustment is constrained by the parameter provided by the experts within the cluster that need adjustment. Finally, an application example of the selection of the hotel for isolating the entry personnel during the Covid-19 pandemic and some comparative analyses are provided to validate the proposed method.

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

A risky large group decision-making method based on FCM clustering and cloud models is proposed for risky large group decision-making problems with linguistic evaluation scales, unknown attribute weights, and many decision members with unknown weights, considering the psychological behavioral characteristics of decision makers’ regret avoidance. The method first uses the golden partition method to improve the cloud model to transform the uncertain linguistic evaluation matrix into a comprehensive cloud model, which quantifies the fuzziness and randomness of linguistic values. The cloud model expectation values are then extracted to determine the attribute weights using the entropy weighting method. Secondly, the three numerical features of the cloud model are extracted as sample features for FCM clustering to obtain the decision maker’s preference clustering information, and the initial weights of decision-makers are determined according to the majority principle, which improves the existing studies that simply use the expected value of the cloud model for clustering analysis, ignoring the entropy and super entropy for portraying the ambiguity and randomness. On this basis, the Hamming distance is introduced to calculate the closeness to adjust the initial weights of decision-makers, improving the way that the weights of aggregation members are equally distributed in previous studies. Finally, considering the influence of the decision maker’s psychological behavior on decision information in the risky decision-making process, regret theory is introduced to construct a decision maker’s perceived utility matrix, which is combined with the decision maker’s weights to determine and rank the combined perceived utility. Through comparison with existing methods, it is found that the proposed method of recalibration of decision-maker preference clustering, while considering the psychological behavior of decision-maker regret avoidance, not only solves the situation of large group decision making in which expert information is easily distorted but also satisfies the convenience of the calculation process and is more suitable for the situation where there are many decision-makers and their preferences are complicated.

Managing Group Confidence and Consensus in Intuitionistic Fuzzy Large Group Decision-Making Based on Social Media Data Mining

Managing Group Confidence and Consensus in Intuitionistic Fuzzy Large Group Decision-Making Based on Social Media Data Mining

Dynamic Fusion Simulation Method of Intertemporal Decision Preferences of Large Groups in Weak Relationships

The fusion of large group decision-making preferences is generally based on hard constraints, and the general low-consensus heterogeneous preference interaction situation of weak relationship decision-making under soft constraints has not been investigated. This study puts forward the preference connotation structure and preference attributes of the intertemporal decision-making of weak relationships groups, objective preference comparison standards between individuals, preference interaction neighborhoods between individuals, preference learning adjustment rules, and preference cycle interaction result evaluation connotations. On this basis, this research constructs a multistage and multiple-round intertemporal decision-making preference dynamic fusion theory and a method for weak relationship large groups. The simulation results verify and support the constructed preference fusion theory and method, indicating that a relatively rational interactive learning and adjustment process can improve the group decision-making performance of the key digital technology R&D team for offshore wind power project construction, operation, and maintenance and effectively improve the convergence level, convergence time, comprehensive decision-making benefits, and decision-making cognition. In the increasingly open large group intertemporal decision-making situation, these research viewpoints and conclusions will help to expand the strong relationship hypotheses of preference fusion in previous group decision-making research. The research opinions and conclusions supplement the theory and method of preference interaction fusion under soft constraints in large group decision-making.

A Novel Noncooperative Behavior Management Method for Multiattribute Large Group Decision-Making.

In multiattribute large-group decision-making (MALGDM), the ideal state indicates a high degree of consensus for decision-makers. However, it is difficult to reach a consensus because the conflict between various decision attributes and decision-makers increases. To deal with the problem, a novel consensus model was developed to manage the decision-making in large groups based on noncooperative behavior. The improved clustering method was used to take account of the similarities among different decision-makers, while similar decision-makers will be grouped into the same group. Moreover, the consensus threshold was determined from an objective and subjective aspect to judge whether the consensus reaching process continues. The noncooperative behavior and adjustment amount of decision-makers' opinions were investigated based on the proposed consensus model, and an emergency decision-making problem in flood disaster is applied to manifest the feasibility and distinctive features of the proposed method. The results show the proposed novel consensus model demonstrated strong applicability and reliability to the noncooperative subgroup problem and can be explored to manage multiattribute interactions in LGDM.

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.

A clustering and fusion method for large group decision making with double information and heterogeneous experts

A clustering and fusion method for large group decision making with double information and heterogeneous experts

Sustainable building material selection: An integrated multi-criteria large group decision making framework

In the past decades, the rapid urbanisation has driven the prosperity of building industry, which, however, has led to the consumption of enormous amounts of energy and resources and the continued deterioration of the environment owing to its unsustainable development. These outcomes impact seriously the ecosystems and human health. In order to promote the sustainable performance of building sector, the widely adoption of sustainable building materials has been considered as one of the most promising ways toward this endeavor. This paper aims at addressing the problem of sustainable building material selection (SBMS) under uncertain context by developing an integrated multi-criteria large-group decision-making framework. In the proposed framework, the assessment information is characterised in the form of Improved Basic Uncertain Linguistic Information (IBULI), which incorporates reliable degrees of decision experts that can accurately quantify subjective assessment information provided under uncertainty. The weights of assessment criteria for SBMS are determined by the Quality Function Deployment-based method that is capable of accommodating the influences of multiple stakeholders and the fields of conflicts amongst them. Subsequently, the assessment information given by a large-group of decision experts are aggregated by IBULI-aggregation operators guided by an IBULI-based correlation- and consensus-driven clustering method, which achieves the classification of large-scale decision experts and the satisfied consensus level simultaneously. The selection of sustainable building materials is eventually accomplished by the IBULI-based Technique for Order Preference by Similarity to Ideal Solution method. Finally, an illustrative example accompanied by sensitivity and comparative analyses is performed to verify the rationality and feasibility of the proposed model.

A non-threshold consensus model based on the minimum cost and maximum consensus-increasing for multi-attribute large group decision-making

This study proposes a non-threshold consensus model that combines the minimum cost and maximum consensus-increasing for multi-attribute large group decision-making (MALGDM). First, the large-scale experts is classified into several clusters via the combination of the similarities of evaluation information, unit consensus cost, and adjustment willingness. Then, a more sensitive consensus measure method that combines the mean value and variance of the similarities among clusters is presented. Next, a comprehensive identification rule is put forward to determine the cluster with a low consensus level, low unit consensus cost, and high adjustment willingness for information adjustment. An optimization model that combines the minimization of the cost of the cluster and the maximization of the increase of the global consensus level is then constructed to obtain the adjusted information. Also, the adjustment willingness is considered in the constraints to limit the adjustment range. Moreover, instead of the use of a predefined threshold and a maximum number of iterations, a termination index is developed to terminate the consensus reaching process (CRP) to make the CRP more objective and rational. Finally, an application example is presented, and comparison and simulation analyses are performed to validate the feasibility and effectiveness of the proposed model.

Study of the Game Model of E-Commerce Information Sharing in an Agricultural Product Supply Chain based on fuzzy big data and LSGDM

Information asymmetry is the root of agricultural product quality problems. Generally speaking, the common problems related to agricultural products are solved by effective supply chain management. From the perspective of agricultural supply chain management, this paper analyzes the shortcomings of the traditional supply chain management process by using fuzzy big data and LSGDM (Large scale group decision making), and puts forward a new method of supply chain management. In addition, from the perspective of asymmetric information sharing regarding agricultural products, an asymmetric information sharing model for agricultural products based on the evolutionary rule theory is established. Combined with fuzzy big data and LSGDM, this paper puts forward the hypothesis of each variable in the process of the information sharing game. On this basis, a mathematical model of the "pool equilibrium" of the high and low prices in the agricultural products market is put forward. Finally, a case study of products of different quality in the sea cucumber market is carried out. The results show that high quality sea cucumber products occupy a large market share in the supply chain of agricultural products. The research of this paper is of great significance, because it can increase the reliable theoretical data available for the research of the agricultural supply chain.

Two-stage consensus model based on opinion dynamics and evolution of social power in large-scale group decision making

The main challenges in large scale group decision making (LSGDM) problem are how to tackle with the great number of participants and how to achieve a common solution accepted by most of participants. To address these challenges, in this paper, we propose a novel framework based on opinion evolution to study the consensus reaching process (CRP) in the LSGDM. In the proposed framework, we focus on the CRP in a dynamical social influence relationship context and the whole CRP is divided into two stages. In the first stage, we design a social power and opinion evolution iterative algorithm to estimate the final consensus opinions in each sub-group. In the second stage, the opinion leaders are selected as the representatives of each sub-group to participate in the consensus process. Subsequently, we develop a self-appraisal mechanism to evaluate the confidence degree of each opinion leader. Furthermore, an opinion leaders’ networked preference evolution mechanism is proposed to investigate the consensus formation of the second stage. Finally, we use this model on a case study and compare it with some existing models.