Multicriterion group classification decision‐making (MCGCDM) involves evaluating alternatives under a specific set of criteria and assigning them to a predetermined set of ordinal categories. One of the main challenges of current research in MCGCDM is establishing a more comprehensive and dynamic feedback adjustment mechanism in the consensus‐reaching process, which ensures the objectivity of the output while better preserving the true intentions of the decision‐makers (DMs). Therefore, this paper constructs a consensus‐driven MCGCDM method based on dynamic trust interaction and overlapping communities’ perspectives. First, the latent factor model algorithm is introduced into the social trust network (STN) based on overlapping community detection to receive the community relations between DMs. Second, according to the trust value and community connection of DMs in STN, a comprehensive, objective, and dynamic experts’ weights calculation method is proposed. Besides, a trust evolution method relying on classification similarity is developed, achieving dynamic updates of trust networks and overlapping communities. A personalized consensus optimization guided by an adaptive adjustment mechanism is designed, emphasizing the significance of overlapping DMs and group opinions while strictly adhering to minimal adjustment. Subsequently, the green building rating is used as an explanatory case to prove the feasibility and objectivity of this method. Eventually, through a series of comparative analyses, the effectiveness and advantages of the proposed method are verified.

With the application of multisource information in different fields, the combination of different types of information, such as numerical data and text information, has become a favourable choice for performing stock market analyses. Despite the rich information provided by multisource data, building structured relationships remains challenging. In addition, some market relationship-based analysis methods use a predefined graph structure as a stock relationship graph, which makes it impossible to sensitively aggregate attribute features, and these methods cannot dynamically update market relationships or relationship strengths. In this paper, we propose a novel dynamic attribute-driven graph attention network incorporating sentiment (AGATS) information, transaction data, and text data. Inspired by behavioural finance , we separately extract sentiment information as a factor of technical indicators, and further realize the early fusion of technical indicators and textual data through tensor fusion. In particular, real-time intramarket dependencies and key attribute information are captured with graph networks, enabling dynamic relationship and relationship strength updates. Experiments conducted on real datasets show that our model is capable of ourperforming previously developed methods in prediction and trading.

When dealing with large-scale group decision-making problems, the central emphasis lies in the objective and rational acquisition of a collective opinion acceptable to the majority of decision makers . Manipulative and overconfident behaviours are two common behaviours that make the decision results deviate from the objective facts in the decision-making process. To manage manipulative and overconfident behaviours in decision-making, this paper investigated a novel consensus model based on social networks. A novel efficient clustering model is first proposed in the model, in which the subgroups' combined cohesion is considered. Furthermore, for the manipulative behaviour of decision makers, we proposed an improved method for the identification and management based on trust relationships. In the consensus reaching process, we proposed a new identification mechanism to promote consensus reaching effectively. In the feedback mechanism, the social network DeGroot model is employed to adjust the opinions of decision makers. Moreover, a management approach is proposed for the overconfident behaviour of decision makers in the social network DeGroot model. Lastly, the feasibility and applicability of the proposed model are verified by an illustrative example. Simulation experiments and comparative analysis demonstrate the effectiveness of the model in facilitating consensus reaching.

Stock prediction is a challenging task due to multiple influencing factors and complex market dependencies. Traditional solutions are based on a single type of information. With the success of multi-source information in different fields, the combination of different types of information such as numerical and textual information has become a promising option. Although multi-source information provides rich multi-view information, how to mine and construct structured relationships from them is a difficult problem. Specifically, most existing methods usually extract features from commonly used multi-source information as predictive information sources, without further pre-constructing stock relationship graphs with dependencies using broader information. More importantly, they typically treat each stock as an isolated forecasting, or employ stock market correlations based on a fixed predefined graph structure, but current methods are not sensitive enough to aggregate the attribute features extracted from multi-source information and stock relationship graph, to obtain the dynamic update of market relations and relationship strength. The stock market is highly temporally, and the attributes of nodes are affected by the time perception of other attributes, which is not fully considered. To address these problems, we propose a novel dynamic attributes-driven graph attention networks incorporating sentiment (DGATS) information, transaction data, and text data. Inspired by behavioral finance, we separately extract sentiment information as a factor of technical indicators, and further realize the early fusion of technical indicators and textual data through Kronecker product-based tensor fusion. In particular, by LSTM and temporal attention network, the short-term and long-term transition features are gradually grasped from the local composition of the fused stock trading sequence. Furthermore, real-time intra-market dependencies and key attributes information are captured with graph networks, enabling dynamic updates of relationships and relationship strengths in predefined graphs. Experiments on the real datasets show that the architecture can outperform the previous methods in prediction performance.

The main aim of the proposed work is to develop the new technique based on decision-theoretical rough sets (DTRSs) and their applications in three-way decision-making problems. This study first develop a fractional fuzzy set (FFS) and their operations, the FFS is a more generalized and accurate tool for describing uncertainty in real-life data information. A new form of decision technique for dealing with the issue of choice based on DTRSs is included in the three-way decisions. The loss function of DTRSs is being used in the proposed decision method model. Initially, the idea of fractional fuzzy α-covering (FF α-covering), fractional fuzzy α–neighborhood (FF α–neighborhood) was introduced. Under the fractional fuzzy state, we integrated the loss function of DTRSs with covering-based fractional fuzzy rough sets. Furthermore, we proposed and established performance characteristics for a new fractional fuzzy α-covering decision-theoretical rough sets model (FFCDTRSs). Then, according to the level of fractional fuzzy numbers (FFN's) positive and negative membership and related three-way decision-making, four methods to solve the expected loss expressed in the form of (FFNs) are described. We have developed a multicriteria decision algorithm (MCDM) based on FFCDTRS. Then an example is used to prove the feasibility of the four methods to solve the MCDM problem. Finally, the results of four distinct decision procedures with various loss functions are compared. The proposed three-way decision-making models are more accurate as compared with particular fuzzy sets.

Handing computing assets to cloud service providers (CSPs) to obtain cloud services is one of the important strategies for enterprises to embrace the digital era, and CSP selection is a crucial decision-making process for cloud deployment. However, there are many criteria involved in selecting an optimal CSP, not all of which can be accurately quantified. Therefore, CSP selection is a typical hybrid-information decision-making problem, in which criterion evaluation values are expressed in various forms. Meanwhile, the psychological behavior of the CSP selection team also has a significant impact on the decision-making result, which is poorly considered in the existing research results on CSP selection. Thus, in this paper, a new group decision-making support framework incorporating regret theory is constructed to select CSPs with hybrid information. Initially, various forms of hybrid information are processed separately to avoid the distortion of heterogeneous information caused by traditional conversion methods. Then, considering the psychology of regret aversion, the respective regret–rejoice functions for hybrid information are defined. Subsequently, regret–rejoice values are introduced into the evaluation of mixed data method framework, and a decision-making support procedure based on it is established, in which an expert weight determination method based on the maximizing consensus model is proposed, and the group best–worst method is used to calculate criteria weights. Afterwards, an illustrative example of CSP selection is given to clarify the implementation process of the proposed method. Finally, the effectiveness and superiority of the proposed decision-making framework in selecting CSPs are explained through parameter analysis and comparison with existing methods.

The selection and evaluation of fresh food suppliers is the primary problem for supermarkets. This problem is solved by a new multiattribute group decision-making method with multigranular unbalanced hesitant fuzzy linguistic term set, which is based on prospect theory and evidential theory. Then, a novel supermarket fresh food supplier evaluation index is constructed with 5 elements and 15 indicators. To get the reasonable weights, best–worst method, criteria importance through intercriteria correlation method, and game theory are applied. Moreover, three methods are compared to prove our proposed method's validity and superiority. Finally, this paper offers analysis and suggestions based on the results from strategy level and statics level, respectively.

Normal wiggly hesitant fuzzy set (NWHFS) is a new fuzzy information form to help decision makers (DMs) express their evaluations, which can further dig the potential uncertain information hidden in the original data given by the DMs. Firstly, we define a new distance measure and new operational laws of NWHFSs. Then, for the situation where attribute weights are completely unknown, we propose an extended CCSD method to produce them objectively, which comprehensively uses standard deviation (SD) and correlation coefficient (CC). What's more, we introduce the MABAC (multiattributive border approximation area comparison) method, which takes the distance between alternatives and the border approximation area (BAA) into consideration for handling the complex and uncertain decision-making problems. Meanwhile, we combine the MABAC method with prospect theory (PT), which considers DMs' psychological behavior, and propose a new NWHF-CCSD-PT-MABAC method to cope with the multi-attribute decision making problems under normal wiggly hesitant fuzzy environment. Lastly, we illustrate the validity and advantages of the proposed method through an example of college book supplier selection.

In this paper, we first introduce a new type of rough sets called α -upward fuzzified preference rodownward fuzzy preferenceugh sets using upward fuzy preference relation. Thereafter on the basis of α -upward fuzzified preference rough sets, we propose approximate precision, rough degree, approximate quality and their mutual relationships. Furthermore, we presented the idea of new types of fuzzy upward β -coverings, fuzzy upward β -neighborhoods and fuzzy upward complement β -neighborhoods and some relavent properties are discussed. Hereby, we formulate a new type of upward lower and upward upper approximations by applying an upward β -neighborhoods. After employing the upward β -neighborhoods based upward rough set approach to it any times, we can only get the six different sets at most. That is to say, every rough set in a universe can be approximated by only six sets, where the lower and upper approximations of each set in the six sets are still lying among these six sets. The relationships among these six sets are established. Subsequently, we presented the idea to combine the fuzzy implicator and t -norm to introduce multigranulation ( ℐ , T ) -fuzzy upward rough set applying fuzzy upward β -covering and some relative properties are discussed. Finally we presented a new technique for the selection of medicine for treatment of coronavirus disease (COVID-19) using multigranulation ( ℐ , T ) -fuzzy upward rough sets.

In practical decision-making problems, the coexistence of several complex situations increases the difficulty for decision makers to make reasonable decision, such as attributes outnumber alternatives, heterogeneous relationships among multiple attributes, and individual risk tendency of decision maker. In view of the advantage of probabilistic linguistic term sets (PLTSs) in presenting qualitative information, a novel decision-making approach with PLTSs is constructed to deal with the above special situations simultaneously. To realize this goal, some basic models have been proposed. First of all, to truly reflect the importance of attributes from the heterogeneous relationships, a weight determination model with generalized Banzhaf values is developed to analyze the interaction between combinations of attributes. Then, for analyzing the individual risk tendency of decision maker, the generalized Banzhaf TODIM method with PLTSs is constructed. Moreover, based on the above research results, the generalized Banzhaf TODIM-QUALIFLEX method with PLTSs is developed to solve decision-making problems where the number of attributes exceeds the number of alternatives, the combinations of attributes are interacted with each other, and decision maker is affected by individual risk propensity. Lastly, smartphones selection through online ratings is a typical case of decision-making problems with the above situations, which is designed to illustrate the performance of the proposed method. And its rationality and advantages are further demonstrated through some comparative analyses with other methods.

As a useful information representation tool, hesitant fuzzy linguistic term set (HFLTS) allows decision makers (DMs) to express their cognitive preferences in terms of several ordered and continuous linguistic terms. Considering the fact that much valuable information related to the cognitive behavior of DMs is hidden in the original evaluation information, this paper studies how to comprehensively mine uncertain information from original hesitant fuzzy linguistic evaluation information given by DMs. To address this objective, we present a new representation tool, normal wiggly hesitant fuzzy linguistic term set (NWHFLTS), which not only retains the original evaluation information, but also delivers and quantifies potential uncertain information, and can also help DMs express their evaluation information in a more complete manner. First, we develop the basic operations, score function, and comparison rule of NWHFLTS based on linguistic scale functions (LSFs), and propose the projection measure, the normal projection measure, and the normalized projection-based distance measure to describe the degree of deviation between two NWHFLTSs. Furthermore, for the case when the attribute weight is completely unknown, we combine the multiattributive border approximation area comparison (MABAC) method and develop a new method called as normal wiggly hesitant fuzzy linguistic projection-based MABAC to solve the multiattribute decision-making problems where attribute values are expressed in the form of NWHFLTS. Finally, through a practical example of marine ecological security situation, the specific calculation steps of this method are exemplified, the feasibility and advancement of the proposed method are demonstrated via a comprehensive comparative study.

The two-dimensional uncertain linguistic variables (2DULVs) add a self-evaluation on the reliability of the assessment results given by decision makers (DMs), so they can better describe some uncertain information, and the partition Bonferroni mean (PBM) operator has the advantages, which assumes that all aggregated arguments are partitioned into several subparts, and members in the same subpart are interrelated and members in different subparts are no interrelationships. However, the traditional PBM can only deal with the crisp numbers and cannot aggregate the 2DULVs. In this paper, we extend the PBM operator to deal with the 2DULVs and propose some PBM operators for 2DULVs. First, we introduce the concepts, properties, operational laws, and comparison methods of 2DULVs, and then we propose the PBM operator for 2DULVs (2DULPBM), the weighted PBM operator for 2DULVs (2DULWPBM), the partitioned geometric Boferroni mean (PGBM) operator for 2DULVs (2DULPGBM), and weighted PGBM operator for 2DULVs (2DULWPGBM). Further, we develop a method to solve multiattribute group decision-making (MAGDM) problems with the 2DULVs. Finally, we give an example to verify that the method based on the proposed operators is effective and influential.

In this paper, a new conception of linguistic q-rung orthopair fuzzy number (Lq-ROFN) is proposed where the membership and nonmembership of the q-rung orthopair fuzzy numbers ( q-ROFNs) are represented as linguistic variables. Compared with linguistic intuitionistic fuzzy numbers and linguistic Pythagorean fuzzy numbers, the Lq-ROFNs can more fully describe the linguistic assessment information by considering the parameter q to adjust the range of fuzzy information. To deal with the multiple-attribute group decision-making (MAGDM) problems with Lq-ROFNs, we proposed the linguistic score and accuracy functions of the Lq-ROFNs. Further, we introduce and prove the operational rules and the related properties characters of Lq-ROFNs. For aggregating the Lq-ROFN assessment information, some aggregation operators are developed, involving the linguistic q-rung orthopair fuzzy power Bonferroni mean (BM) operator, linguistic q-rung orthopair fuzzy weighted power BM operator, linguistic q-rung orthopair fuzzy power geometric BM (GBM) operator, and linguistic q-rung orthopair fuzzy weighted power GBM operator, and then presents their rational properties and particular cases, which cannot only reduce the influences of some unreasonable data caused by the biased decision-makers, but also can take the interrelationship between any two different attributes into account. Finally, we propose a method to handle the MAGDM under the environment of Lq-ROFNs by using the new proposed operators. Further, several examples are given to show the validity and superiority of the proposed method by comparing with other existing MAGDM methods.

Linguistic q-rung orthopair fuzzy numbers (Lq-ROFNs) are a qualitative form of q-rung orthopair fuzzy numbers (q-ROFNs) where the membership and nonmembership degrees are represented by linguistic variables. The Lq-ROFNs can describe a broader range of linguistic assessment information flexibly by adjusting the parameter q based on different situations, so they are more superior to the linguistic intuitionistic fuzzy numbers and linguistic Pythagorean fuzzy numbers in real application. Based on the Lq-ROFNs, we introduce the entropy measure which can be used to determine the indefiniteness of the assessment information. Then, based on the linguistic entropy measure, we further propose a method to obtain the attribute weights when the weight information is incomplete known. For aggregating the assessment information, the power average (PA) operator can reduce the influence of extreme data caused by the biased decision-makers by considering the support degree of different evaluation individuals, and the Muirhead mean (MM) operator can take the interrelationship of different numbers of attributes into account by adjusting the parameter vector based on the real situations. In this paper, based on these two operators, we firstly propose the linguistic q-rung orthopair fuzzy PA operator and linguistic q-rung orthopair fuzzy weighted PA operator. Further, for combing the advantages of the MM operator and PA operator, we propose the linguistic q-rung orthopair fuzzy power MM (PMM) operator and linguistic q-rung orthopair fuzzy weighted PMM operator, and then investigate some properties of them. Finally, a new multiple-attribute group decision-making (MAGDM) method is proposed to process the Lq-ROFNs, and some practical examples are given to illustrate the effectiveness and superiority of this new method in comparison with other existing MAGDM methods.

With the continuous development of the economy and society, decision-making problems and decision-making scenarios have become more complex. The q-rung orthopair fuzzy set is getting more and more attention from researchers, which is more general and flexible than Pythagorean fuzzy set and intuitionistic fuzzy set under complex vague environment. In this study, the concept of q-rung orthopair fuzzy linguistic set (q-ROFLS) is proposed and a new q-rung orthopair fuzzy linguistic method is developed to handle MAGDM problem. Firstly, the conception, operation laws, comparison methods, and distance measure methods of the q-ROFLS are proposed. Secondly, the q-ROFL weighted average operator, q-ROFL ordered weighted average operator, q-ROFL hybrid weighted average operator, q-ROFL weighted geometric operator, q-ROFL ordered weighted geometric operator, and q-ROFL hybrid weighted geometric operator are proposed, and some interesting properties, special cases of these operators are investigated. Furthermore, a new method to cope with MAGDM problem based on q-ROFL weighted average operator (q-ROFL weighted geometric operator) is developed. Finally, a practical example for suppliers selection is provided to verify the practicality of the presented method, and the effectiveness and flexibility of the presented method are illustrated by sensitive analysis and comparative analysis.

In the real multi-attribute group decision making (MAGDM), there will be a mutual relationship between different attributes. As we all know, the Bonferroni mean (BM) operator has the advantage of considering interrelationships between parameters. In addition, in describing uncertain information, the eminent characteristic of q-rung orthopair fuzzy sets (q-ROFs) is that the sum of the qth power of the membership degree and the qth power of the degrees of non-membership is equal to or less than 1, so the space of uncertain information they can describe is broader. In this paper, we combine the BM operator with q-rung orthopair fuzzy numbers (q-ROFNs) to propose the q-rung orthopair fuzzy BM (q-ROFBM) operator, the q-rung orthopair fuzzy weighted BM (q-ROFWBM) operator, the q-rung orthopair fuzzy geometric BM (q-ROFGBM) operator, and the q-rung orthopair fuzzy weighted geometric BM (q-ROFWGBM) operator, then the MAGDM methods are developed based on these operators. Finally, we use an example to illustrate the MAGDM process of the proposed methods. The proposed methods based on q-ROFWBM and q-ROFWGBM operators are very useful to deal with MAGDM problems.

A Q-rung orthopair fuzzy set (q-ROFS) originally proposed by Yager (2017) is a new generalization of orthopair fuzzy sets, which has a larger representation space of acceptable membership grades and gives decision makers more flexibility to express their real preferences. In this paper, for multiple attribute decision-making problems with q-rung orthopair fuzzy information, we propose a new method for dealing with heterogeneous relationship among attributes and unknown attribute weight information. First, we present two novel q-rung orthopair fuzzy extended Bonferroni mean (q-ROFEBM) operator and its weighted form (q-ROFEWEBM). A comparative example is provided to illustrate the advantages of the new operators, that is, they can effectively model the heterogeneous relationship among attributes. We prove that some existing known intuitionistic fuzzy aggregation operators and Pythagorean fuzzy aggregation operators are special cases of the proposed q-ROFEBM and q-ROFEWEBM operators. Meanwhile, several desirable properties are also investigated. Then, a new knowledge-based entropy measure for q-ROFSs is also proposed to obtain the attribute weights. Based on the proposed q-ROFWEBM and the new entropy measure, a new method is developed to solve multiple attribute decision making problems with q-ROFSs. Finally, an illustrative example is given to demonstrate the application process of the proposed method, and a comparison analysis with other existing representative methods is also conducted to show its validity and superiority.

The q-rung orthopair fuzzy sets (q-ROFs) are an important way to express uncertain information, and they are superior to the intuitionistic fuzzy sets and the Pythagorean fuzzy sets. Their eminent characteristic is that the sum of the qth power of the membership degree and the qth power of the degrees of non-membership is equal to or less than 1, so the space of uncertain information they can describe is broader. Under these environments, we propose the q-rung orthopair fuzzy weighted averaging operator and the q-rung orthopair fuzzy weighted geometric operator to deal with the decision information, and their some properties are well proved. Further, based on these operators, we presented two new methods to deal with the multi-attribute decision making problems under the fuzzy environment. Finally, we used some practical examples to illustrate the validity and superiority of the proposed method by comparing with other existing methods.

The q-rung orthopair set (q-ROFSs) can serve as a generalization of the existing orthopair fuzzy sets, including intuitionistic fuzzy sets and Pythagorean fuzzy sets. The most desirable characteristic of q-ROFSs is that they support a greater space of allowable membership grades and provide decision makers more freedom in describing their true opinions. As a classical aggregation operator, Heronian mean (HM) can model the interrelationship between attributes. In this paper, we extend the traditional HM to aggregate q-rung orthopair fuzzy information and propose the q-rung orthopair fuzzy HM and its weighted form. Further, to overcome the shortcomings of the traditional HM, considering the possible partition structure in the actual decision situations, we propose the q-rung orthopair fuzzy partitioned Heronian mean operator and the q-rung orthopair fuzzy weighted partitioned Heronian mean operator. Then, some special cases and some desirable properties are investigated and discussed. A new multiple attribute group decision-making(MAGDM) technique is developed based on the proposed q-rung orthopair fuzzy operators. Finally, a representative example is provided to verify the effectiveness and superiority of the proposed method by comparing with other several existing representative MAGDM methods.

The power average (PA) operator and Maclaurin symmetric mean (MSM) operator are two important tools to handle the multiple attribute group decision-making (MAGDM) problems, and the combination of two operators can eliminate the influence of unreasonable information from biased decision makers (DMs) and can capture the interrelationship among any number of arguments. The Pythagorean fuzzy linguistic set (PFLS) is parallel to the intuitionistic linguistic set (ILS), which is more powerful to convey the uncertainty and ambiguity of the DMs than ILS. In this paper, we propose some power MSM aggregation operators for Pythagorean fuzzy linguistic information, such as Pythagorean fuzzy linguistic power MSM operator and Pythagorean fuzzy linguistic power weighted MSM (PFLPWMSM) operator. At the same time, we further discuss the properties and special cases of these operators. Then, we propose a new method to solve the MAGDM problems with Pythagorean fuzzy linguistic information based on the PFLPWMSM operator. Finally, some illustrative examples are utilized to show the effectiveness of the proposed method.