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Online reviews published on the e-commerce platform provide a new source of information for designers to develop new products. Past research on new product development (NPD) using user-generated textual data commonly focused solely on extracting and identifying product features to be improved. However, the competitive analysis of product features and more specific improvement strategies have not been explored deeply. This study fully uses the rich semantic attributes of online review texts and proposes a novel online review–driven modeling framework. This new approach can extract fine-grained product features; calculate their importance, performance, and competitiveness; and build a competitiveness network for each feature. As a result, decision making is assisted, and specific product improvement strategies are developed for NPD beyond existing modeling approaches in this domain. Specifically, online reviews are first classified into redesign- and innovation-related themes using a multiple embedding model, and the redesign and innovation product features can be extracted accordingly using a mutual information multilevel feature extraction method. Moreover, the importance and performance of features are calculated, and the competitiveness and competitiveness network of features are obtained through a personalized unidirectional bipartite graph algorithm. Finally, the importance performance competitiveness analysis plot is constructed, and the product improvement strategy is developed via a multistage combined search algorithm. Case studies and comparative experiments show the effectiveness of the proposed method and provide novel business insights for stakeholders, such as product providers, managers, and designers. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: This work was supported by the National Natural Science Foundation of China [Project 72071151] and the Natural Science Foundation of Hubei Province, China [Grant 2023CFB712]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0333) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0333). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

In minimum cost consensus problems, to accomplish the group consensus, decision makers accommodate their initial discrepant opinions based on the unit adjustment costs. During this process, decision makers may exhibit loss aversion, which results in extra expenses when participants feel loss in contrast to their reference opinion adjustments. Existing minimum cost consensus models pay little attention to the loss-averse preference. Hence, to fill up this gap, a minimum cost consensus model with loss aversion (MCCM-LA) is established and the desired properties are analyzed. To manage large-scale group decision making problems, we first propose an integrated opinion similarity, connectivity similarity and behavior similarity clustering algorithm to divide decision makers into multiple subgroups. Balancing the individual adjustment willingness and consensus reaching efficiency, a two-stage consensus reaching mechanism is further designed based on MCCM-LA to realize the accordant opinion. Finally, the effectiveness and feasibility of the proposed method are demonstrated by sensitivity and comparative analyses with an illustrative example. [ABSTRACT FROM AUTHOR]

In modern logistics, port transportation plays an important role in the development of the surrounding economy. However, to determine the level of port group competitiveness, regional ports must be classified. To realize this classification, this paper introduces a novel multiple criteria sorting method, ORESTE-SORT, together with its main characteristics and properties. This approach can help in handling three types of preference relationships among alternatives and profiles: preference, indifference, and incomparability. In addition, a novel sorting rule, the assignment rule driven by attitudes (ARDA), is introduced to allocate alternatives to predefined categories based on the generalization of two classical sorting rules: optimistic and pessimistic rules. We also perform a case study to illustrate the suitability of the proposed sorting method for sorting port group competitiveness. Finally, sensitivity analysis and comparative analysis are performed with the ELECTRE-SORT method to determine the effectiveness of ORESTE-SORT, following which the capability of this new sorting methodology is further discussed. [ABSTRACT FROM AUTHOR]

In the elicitation of decision makers' fuzzy and uncertain assessments, linguistic terms are natural and efficient as the preference modeling tools. Although the linguistic variables are available, they would not be operational without any detailed quantification. Motivated by the flexibility of information granularity, this paper develops information granules to represent linguistic terms in the form of intervals and interval type-2 fuzzy sets (IT2FSs) in best worst method (BWM). The development is aimed at minimizing inconsistency in the decision making (DM) process to ensure the rationality of the assessments provided by decision makers. Furthermore, the input and output based consistencies of BWM are considered. The granulation of entries of pairwise comparison vectors are the foundation of BWM to formulate an optimization problem where particle swarm optimization (PSO) algorithm serves as the optimization framework. Both individual and group decision making (GDM) scenarios are taken into consideration. For the GDM process, a performance index for measuring the group consensus is also proposed. Several examples and validity analysis are covered to illustrate the major ideas of this study. Finally, as a case study, a recommendation of the sequence of visiting tourist attractions in Wuhan and the corresponding comparative analysis are represented. • A granular computing-based method in BWM framework is proposed. • Construction of granular linguistic information to represent preference information of DMs is given. • A novel Euclidean distance function to measure the agreement among the group is proposed. • A case study of scenic spots recommendation in Wuhan with online reviews is exhibited. [ABSTRACT FROM AUTHOR]

In group decision-making processes, decision makers may have different risk preference types, which usually make them take various responses to the same problem. In minimum cost consensus problems, risk seekers, risk neutrals and risk averters may exhibit individual cost adjustment characteristics, which require aggregating their opinions in a model. To solve this problem, this paper presents a minimum cost consensus model to obtain group consensus preferences using a general optimization model, which can handle three types of risk preferences in decision making. In addition, a data-driven weighting scheme for the degrees of importance of decision makers is developed, and a generalized group consensus opinion aggregation function is designed. The proposed group consensus opinion aggregation function extends the existing developed opinion production method, which is more compatible than other aggregation functions. Finally, the proposed minimum cost consensus with risk preferences model is verified through an illustrative example. [ABSTRACT FROM AUTHOR]

Social network group decision-making (SNGDM) develops rapidly because of the popularity of online connections among experts. The current SNGDM studies mainly focus on the influence of trust on the evolution of consensus opinions through a complex process. Some situations are ignored in the existing research when trust may negatively affect the decision-making process and trust relationships may be developed or interrupted during consensus negotiation processes. To overcome such limitations, we propose a consensus model based on the trust-consensus multiplex network by combing trust social network analysis and consensus evolution networks (CENs). We design an interaction mechanism between trust and consensus based on the dynamic experts’ influence which is computed by the multiplex PageRank centrality measure, especially focusing on the negative impact of trust on consensus. Besides, we compute consensus levels based on the density and intensity of CENs to determine when should the negotiation end. The proposed model can not only consider the mutual influence between trust and consensus in SNGDM, but also detect and analyze the negative influence of trust on consensus. An example examines the effectiveness of the proposed model and a comparative analysis shows its flexibility for studying the complex consensus situation of SNGDM. [ABSTRACT FROM AUTHOR]

In large-scale group decision making (LSGDM), appropriate clustering analysis is important to consensus reaching since it can reduce the interactive complexity among individuals. According to the traditional clustering method, a conflict may arise between the consensus reaching levels and total adjustment costs within clusters when individuals have different unit adjustment cost, which reflects their willingness to make concessions. Since this conflict may aggravate the consensus complexity, we propose a new $K$ -means clustering method that considers both preferences and the preference adjustment cost. The preference adjustment cost is attached to preferences with a parameter that can be determined by balancing this conflict. Because of such conflict, the proposed clustering algorithm can improve the similarity of intracluster individuals on the preference adjustment cost by offsetting some acceptable consensus reaching levels within clusters. According to the proposed clustering algorithm, individuals who have both similar preferences and adjustment willingness are classified into the same clusters. In this way, the moderator can provide similar compensation strategies for intracluster individuals, which will decrease the adjustment complexity. A practical case study of team construction examines the application of the proposed algorithm, and the related comparative analysis shows that it is convenient for managers to persuade individuals to reach a consensus under the improved clustering results. [ABSTRACT FROM AUTHOR]

In most existing linguistic distribution multiple criteria group decision-making (MCGDM) models, the sample size of group linguistic distribution assessment (LDA) is often neglected, and the decision makers are viewed to be independent. The probabilistic distribution is closely related to the sample size, and there exist more or fewer interference effects among different individual opinions. Thus, this article develops a quantum framework for modeling interference effects in the linguistic distribution MCGDM process. First, considering the sample size information, we redefine the LDAs and provide a new computational model for them. Second, to integrate a series of LDAs, the linguistic distribution weighted averaging (LDWA) operator is presented. Mathematic proofs reveal that the new LDWA operator can not only ensure the information integrality but also can avoid the conjunctive and disjunctive operations. Third, to explore the interference effects among individual opinions, a quantum framework is constructed. In this process, individual opinions are viewed as various wave functions occurring synchronously. They interfere with each and influence the aggregation result. Finally, an illustrative example of Internet finance soft power evaluation is provided to verify the effectiveness. Sensitivity and comparative analyses are also implemented to assess the stability and validity of our method. [ABSTRACT FROM AUTHOR]

Ranking of fuzzy numbers (FNs) is a key stage within fuzzy multicriteria decision analysis (FMCDA). However, the influence of FNs dependence on their ranking, including ranking alternatives within FMCDA, has not been studied yet. In this article, for studying such an influence, the widely used defuzzification-based fuzzy ranking methods, centroid index, and integral of means, along with their modifications, which are pairwise comparison defuzzification ranking methods, are explored. The authors argue that classical defuzzification ranking methods are intended to deal with independent FNs, whereas their modifications may be used for ranking of dependent FNs. It is provided a proof in which, pairwise comparison Yuan’s and defuzzification integral of means ranking methods, are equivalent when ordering independent and can differ when ordering dependent FNs; at the same time, Yuan’s and modified integral of means ranking methods are equivalent when ordering both independent and dependent FNs. Intransitivity of the two modified ranking methods when ordering dependent FNs as well as intransitivity of alternatives in FMCDA for fuzzy multiattribute value theory (FMAVT) as an example is proved. The distinctions in ranking of dependent FNs by all ranking methods under consideration are explored through ordering alternatives within FMAVT. For this, a real-life case study is considered, and the distinctions in ordering alternatives by classical and modified ranking methods are demonstrated. Statistical analysis of distinctions in ordering alternatives by FMAVT with different ranking methods is implemented with the use of Monte–Carlo simulation. The significance of distinctions for the choice and ranking multicriteria problems, as well as for justification of utilizing ranking methods under consideration in FMCDA, is discussed. [ABSTRACT FROM AUTHOR]

Large-scale group decision-making problems based on social network analysis and minimum cost consensus models (MCCMs) have recently attracted considerable attention. However, few studies have combined them to form a complete decision-making system. Accordingly, we define the satisfaction index to optimize the classical MCCM by considering the effect of the group on individuals. Similarly, we define the consistency index to optimize the consensus reaching process (CRP). Regarding the evolution of the consensus network, the Louvain algorithm is used to divide the entire group into several subgroups to ensure that each subgroup is independent but has strong cohesion. By constructing the MCCM based on the satisfaction index and the optimized consensus-reaching process, the group opinions in each subgroup are ranked to obtain the final ranking of alternatives. Finally, to verify the validity of CRP and the practical value of the proposed model, we conduct consensus network evolution and decision-making analysis in the case of a negotiation between the government and polluting companies to achieve uniform pollution emissions. Sensitivity analysis is performed to demonstrate the stability of the subgroup weights. Furthermore, a comparative analysis using existing models verifies the effectiveness of the proposed model. • A new framework of MCCM for LSGDM problems is proposed. • The satisfaction index is defined and used to optimize the MCCM. • The CRP-driven optimization model based on the consistency index is provided. • A new method based on community detection is employed to measure the clustering result. • A case of pollution emissions is conducted to simulate the network evolution. [ABSTRACT FROM AUTHOR]

In this article, we propose the concept of flexible linguistic preference relations (FLPRs), in which the flexible linguistic expressions, a more flexible way to form linguistic expressions, are employed. Further, we present a method to rank alternatives based on preference information in FLPRs by exploring the linguistic distribution (LD) and priority-based approximation (PA) of FLPRs. In the LD-based approximation, we first present a two-stage optimization process to approximate FLPRs to distribution linguistic preference relations (DLPRs) following the principles of minimum preference loss and maximum consistency. Then in the PA process, we derive priority vectors from the DLPRs with some desired properties. Finally, a comparative analysis of the priority vectors derived from different kinds of linguistic preference relations is presented to illustrate our proposal. [ABSTRACT FROM AUTHOR]

Large-scale group decision-making (LSGDM) solution is usually based on the clustering analysis process (CAP) and consensus reaching process (CRP). However, CAP and CRP can be contradictory since CAP is performed based on the differences between potentially small groups and CRP is conducted to improve the overall similarity of a large group. To balance CAP and CRP, a dynamic clustering analysis process (DCAP) based on consensus evolution networks is proposed. A clustering algorithm proposed based on community detection method can be used to handle the diverse network structures with dynamic consensus thresholds. The clustering validity based on the intracluster consensus levels in subgroups and the intercluster consensus level among subgroups is evaluated. Then, the DCAP after each feedback adjustment round in CRP is reanalyzed. In such a way, effective clustering can also be found after a satisfying consensus is reached. Finally, a case study shows the availability of this approach and comparative analyses are provided to highlight the advantages from both theoretical and numerical perspectives. [ABSTRACT FROM AUTHOR]

In this article, we deal with an improved fusion method for multigranular linguistic distribution assessments, a flexible and practical expression, in a multiple criteria group decision-making problem, which permits an accurate transformation between two linguistic labels without the loss of information. The importance degree of decision makers is allocated on the basis of individual consistency elicited from evaluation assessments represented by linguistic distributions, which is described with the aid of the proposed dominance degree of linguistic distribution. To intuitively reflect the group consensus level, a new measure, Jaccard consensus of linguistic distribution, is introduced. Based on this, a consensus-based multicriteria group preference analysis model with multigranular linguistic distribution information is established within a unified framework, which is developed by means of additive multiattribute value utility theory. Furthermore, a corresponding algorithm is matched to obtain a representative value function for decision analysis and an illustrative example is developed to illustrate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Various linguistic expressions have been presented to model the flexibility of linguistic preference expressions and to support the consensus reaching in linguistic group decision-making (GDM). In this paper, we propose the concept of flexible linguistic expressions (FLEs) as a general linguistic preference expression format to improve the flexibility of the construction of complex linguistic expressions and the elicitation of linguistic preferences and, then, we develop a new linguistic GDM model with FLEs, referred to as FLE-based GDM (FLEGDM). In the FLEGDM, an FLE aggregation process with accurate constraints is developed to improve the quality (i.e., accuracy) of the collective result as well as guarantee the principle of minimum preference-loss through a mixed 0–1 linear programming model. Meanwhile, the consensus rules with minimum preference-loss are designed to support the consensus reaching process (CRS) in the FLEGDM. Finally, we present the detailed comparative analysis involving different linguistic GDM models to show the advantages of the FLEGDM. [ABSTRACT FROM AUTHOR]

Failure Mode and Effect Analysis (FMEA) is considered as a proactive risk prevention and control technique that has been widely applied to identify, assess and eliminate the risk of failure modes in various fields. Nevertheless, the interactions between risk indicators and decision maker's psychological behavior characteristics are seldom considered simultaneously in the current FMEA method. In this article, we develop a hybrid FMEA framework integrating generalized TODIM (an acronym in Portuguese of Interactive and Multi-criteria Decision Making) method, Choquet integral and Shapley index to remedy this gap. In the proposed FMEA framework, fuzzy measures and Shapley index are used to model the interaction relationships among risk indicators and to determine the weights of these indicators. The extended generalized TODIM method with fuzzy measure and Shapley index is presented to simulate the psychological behavior characteristics of FMEA team members. It is also applied to calculate the risk priority of each failure mode. Trapezoidal Fuzzy Numbers (TrFNs) are adopted to depict the uncertainty in the risk evaluation process. Furthermore, a new risk evaluation information fusion with TrFNs-WAIA (weighted arithmetic interaction averaging operator of the trapezoidal fuzzy numbers) operator based on Shapley Choquet is developed to aggregate individual risk evaluation information of FMEA team member into a group risk evaluation matrix, which considers the potential correlations among these members. Finally, a practical example of FMEA problem is presented to demonstrate the application and feasibility of the proposed hybrid FMEA framework, and comparison and sensitivity studies are also conducted to validate the effectiveness of the improved FMEA approach. [ABSTRACT FROM AUTHOR]

Trust is crucial for purchasing decisions in social commerce. However, inexperienced users may not have a direct trust relation to experienced users in practice. Besides, users tend to give their trust degrees to others with linguistic labels rather than crisp values. To evaluate the trust degree for inexperienced users to experienced ones, we propose an interval type‐2 fuzzy trust evaluation model in this paper. Interval type‐2 fuzzy linguistic labels are used to represent trust degree among users. An interval type‐2 fuzzy Algebraic t‐norm is addressed to compute propagative trust degrees. Considering the effect of trust path length, the induced ordered weighted averaging (IOWA) operator is extended to aggregate the interval type‐2 fuzzy trust degrees obtained from multiple paths. In addition, the final interval type‐2 fuzzy trust degree is transferred into the corresponding linguistic label to help users make decisions more naturally. Finally, a case study in social commerce and a related comparison are given to verify the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

The management of uncertainty in decision-making problems remains a very challenging and timely research issue despite many proposals. An interesting topic in this area in recent years has been Granular Computing. In this paper, we present a coherent framework for various models of information granules, using hierarchies of information for dealing with knowledge acquired and processed at different levels of abstraction. Granular Computing, a paradigm for handling higher types of uncertainty in decision analysis, is considered a new conceptual and algorithmic asset of decision-making studies and, in particular, data-driven decision-making. Most relevant studies have focused on categories of information granules, namely intervals, fuzzy sets, rough sets, and shadowed sets. To better understand this promising area, this study provides a comprehensive overview of Granular Computing for decision-making through literature analysis, enhancing a variety of extensions, applications, and challenges. • We propose a comprehensive review for granular computing in decision making. • We provide a bibliometric review for granular computing in decision making. • We review main applications in granular computing in decision making. • We provide some future directions for granular computing in decision making. [ABSTRACT FROM AUTHOR]

The digital economy is first and foremost a data economy, and data is the first element of the digital economy. I Management System Engineering (MSE) i is dedicated to the methodology of System Engineering (SE) and the practice of Management Decision Making. The digital economy is a network economy, and the Internet is the basic carrier of the digital economy. [Extracted from the article]

In this work, we propose some two-layer preference models that can be appropriately applied in management problems such as the group decision making about predicting the future market share of certain product. By introducing the convex IOWA operator paradigm and some related properties and definitions, we list some detailed preference and inducing preference models to demonstrate and exemplify the proposed conceptual frame of two-layer preference model. The convex IOWA operator paradigm facilitates the modeling process and, from mathematical view, makes it stricter. When relevant inducing information and aggregation selection change, the proposed models can be easily adapted to accommodate more different applications in decision making and evaluation. [ABSTRACT FROM AUTHOR]

The Failure mode and effect analysis (FMEA) is an effective risk evaluation approach which has been widely used to assist in risk controlling in various workplaces. However, in practice, the conventional FMEA approach suffers from the drawbacks associated with the risk evaluation and priorization methods. In this paper, a novel risk priorization method for FMEA based on the extended MULTIMOORA (Multi-Objective Optimization by Ratio Analysis plus the Full Multiplicative Form) method is proposed. First, the interval type-2 fuzzy sets are applied to deal with the uncertainty of risk evaluation in FMEA. Second, the distance-based method is used to calculate the importance weight of each risk factor. Then, an extended MULTIMOORA method is presented to rank risk priority of each failure mode, in which the distance measure for interval type-2 fuzzy number is incorporated. Finally, a practical case in steel company is selected to illustrate the application and feasibility of the proposed approach. A comparative analysis is conducted to demonstrate the effectiveness of the developed risk priorization method. [ABSTRACT FROM AUTHOR]

Finding the maximum and minimum values of a given collection of real inputs is critical in many decision-making and evaluation models. In often encountered evaluation problems where information is collected based on survey or statistics and embodied by a collection of normalized distributions on lattice, we cannot directly obtain the corresponding concept of “maximum” and “minimum.” To process such situations and related evaluation problem, we first define the distributions poset on a given lattice structure and then propose the concept of lattice dominance points, which can sometimes be seen as a type generalized “maximum” points. Often there may be infinite dominance points to choose from. In order to find some more suitable ones according with our intuition and with mathematical completeness, we propose an optimization method and distribution t-conorm methods to elicit lattice dominances based on given collection of normalized distributions. The distribution and bilinear distribution t-conorms examples are well defined, and some related properties are proposed with strict proofs. In addition, a commonly used evaluation model is proposed with results for comparing differently elicited lattice dominance points. [ABSTRACT FROM AUTHOR]

Supplier selection is a key issue in supply chain management, which directly impacts the manufacturer's performance. The problem can be viewed as a multiple attribute group decision making (MAGDM) that concerns many conflicting evaluation attributes, both being of qualitative and quantitative nature. Due to the increasing complexity and uncertainty of socio-economic environment, some evaluations of attributes are not adequately represented by numerical assessments and type-1 fuzzy sets. In this paper, we develop some linear programming models with the aid of multidimensional analysis of preference (LINMAP) method to solve interval type-2 fuzzy MAGDM problems, in which the information about attribute weights is incompletely known, and all pairwise comparison judgments over alternatives are represented by IT2FSs. First, we introduce a new distance measure based on the centroid interval between the IT2FSs. Then, we construct the linear programming model to determine the interval type-2 fuzzy positive ideal solution (IT2PIS) and corresponding attributes weight vector. Based on it, an extended LINMAP method to solve MAGDM problem under IT2FSs environment is developed. Finally, a supplier selection example is provided to demonstrate the usefulness of the proposed method. [ABSTRACT FROM AUTHOR]

Interval type-2 fuzzy set (IT2FS) offers interesting avenue to handle high order information and uncertainty in decision support system (DSS) when dealing with both extrinsic and intrinsic aspects of uncertainty. Recently, multiple attribute decision making (MADM) problems with interval type-2 fuzzy information have received increasing attentions both from researchers and practitioners. As a result, a number of interval type-2 fuzzy MADM methods have been developed. In this paper, we extend the VIKOR (VlseKriterijumska Optimizacijia I Kompromisno Resenje, in Serbian) method based on the prospect theory to accommodate interval type-2 fuzzy circumstances. First, we propose a new distance measure for IT2FS, which is comes as a sound alternative when being compared with the existing interval type-2 fuzzy distance measures. Then, a decision model integrating VIKOR method and prospect theory is proposed. A case study concerning a high-tech risk evaluation is provided to illustrate the applicability of the proposed method. In addition, a comparative analysis with interval type-2 fuzzy TOPSIS method is also presented. [ABSTRACT FROM AUTHOR]

The Maclaurin symmetric mean (MSM) was originally introduced by Maclaurin and then generalized by Detemple and Robertson. The prominent characteristic of the MSM is that it can capture the interrelationship among the multi-input arguments. However, the researches on MSM are very rare, especially in fuzzy decision making. In this paper, we investigate the MSM operator and extend the MSM operator to intuitionistic fuzzy environment. Some new aggregation operators based on MSM for dealing with intuitionistic fuzzy information are developed, such as the intuitionistic fuzzy Maclaurin symmetric mean (IFMSM) and the weighted intuitionistic fuzzy Maclaurin symmetric mean (WIFMSM). Some desirable properties and special cases of these operators are discussed in detail. Based on WIFMSM operator, an approach to multiple attribute decision making (MADM) problems with intuitionistic fuzzy information is developed. Finally, a practical example is provided to illustrate the practicality and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

• Multi-granular linguistic distribution evidential reasoning method is proposed. • Incomplete and uncertain evaluation is handled in MCGDM problems. • A novel comparison method for linguistic distribution assessments is introduced. • Developed a transformation technique for multi-granular linguistic distribution. • Renewable energy project risk assessment is conducted. Nowadays, renewable energy projects have constantly been emphasized and the assessment of potential risks has been considered as an indispensable activity prior to implementation of projects. To carry out a reasonable assessment, multi-granular linguistic distribution assessments (LDAs), an effective and uncertainty representation tool, are adopted to express and quantify opinions based on personalized individual linguistic term sets. Specifically, this study develops a novel uncertain multiple criteria decision making approach, named multi-granular linguistic evidential reasoning method, which can handle incomplete and personalized preferences. A novel comparison method for LDAs is first put forward based on numerical characteristics, namely an expectation value and central moment. To fuse the multi-granular LDAs, a lossless transformation technique is further introduced and some of itsessential properties are discussed. Finally, a case study on renewable energy project risk assessment is discussed to verify the feasibility of the proposed method. Besides, sensitivity analysis is conducted to investigate the sequencing stability and comparative analysis is included to highlight the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

• Construct consensus evolution networks (CENs) managing consensus thresholds. • Introduce new consensus index numerically and structurally based on CENs. • Design a pairwise adjustment method based on the complementary sensitive CEN. • Give reference for the agreed consensus threshold based on the sparsity of CENs. The consensus reaching process (CRP) is a critical part of group decision making (GDM). In order to explore the evolution of consensus, a new CRP tool is proposed based on consensus evolution networks (CENs). The CENs are built based on the consensus degrees among decision makers (DMs) and allow us to manage the consensus thresholds and its evolution. A new consensus index is introduced based on the structured and numerical aspects of the CENs. The new consensus index can not only deeply analyze the constitution of consensus, but also determine the weights of DMs. According to the clustering coefficient, the sensitive consensus threshold is identified and the sensitive consensus evolution network (SCEN) is built. Based on the complementary SCEN, a pairwise feedback adjustment method is proposed to improve consensus. Besides, the sparsity of the CENs can act as a reference to determine the agreed consensus thresholds, which is considered an important issue in traditional models. A numerical example is used to verify the usefulness of the proposed CRP tool. The numerical results show that the evolution of consensus can be clearly found based on CENs and the pairwise method can improve consensus in only four rounds. [ABSTRACT FROM AUTHOR]

Multi-criteria decision-making (MCDM) models are well-suited for solving portfolio selection problems. Diversified financial indices and complex subjective preferences are important factors affecting investment decisions within the MCDM framework. Therefore, this study adopts a well-known behavioral MCDM model called generalized TODIM (TOmada de Decisão Iterativa Multicritério) for portfolio selection based on the financial performance of firms. First, a multidimensional financial evaluation index system is proposed for financial performance evaluation, which is the most appropriate approach for stock investment within a long-term horizon. Second, features are selected from the financial ratios using affinity propagation clustering (APC). Through the APC algorithm, financial ratios with a strong influence on stock evaluation can be obtained. Third, a generalized TODIM method with entropy weight is used to calculate the dominance relation between stocks and reflect investors' bounded rational behaviors. Fourth, an extended mean–variance multi-objective portfolio selection model that considers financial and stock market performance is constructed. The compromise solution is used when solving the multi-objective optimization programming. Finally, a case study on medical stock investment in the Chinese stock market is examined to recommend the optimal portfolio allocation for investors. Sensitivity and comparative analyses are performed to demonstrate the robustness, effectiveness, and superiority of the proposed methodology. • Develop an integrated generalized TODIM model for portfolio selection. • A multi-dimensional criteria system is put forward for portfolio selection. • A multi-stage generalized TODIM decision mechanism is implemented. • A novel multi-objective MV portfolio selection model is constructed. [ABSTRACT FROM AUTHOR]