Group Decision-making Problems Research Articles

Risk Assessment for Breast Cancer with Integrated Group Decision-Making Method

The most prevalent invasive malignancy in women is breast cancer. The second most common cause of cancer deaths in women, behind lung cancer, is breast cancer. It begins with developing a tiny tumor or mass and spreads from breast cells, primarily in the milk ducts (ductal carcinoma) or glands (lobular carcinoma). Every woman needs to be aware of her risk of developing breast cancer to be proactive about risk reduction measures and for better care of the disease, even though the causes of breast cancer are not fully known. Numerous variables that can either raise or decrease the likelihood of acquiring breast cancer have been identified by independent investigations. By looking at these risk factors, it is feasible to determine a woman's estimated risk of acquiring a malignant breast illness. Fermatean fuzzy sets can adequately describe the uncertain data for determining breast cancer risk. The cumulative prospect theory is used to build the traditional Tomada de Decisão Iterativa Multicritério (TODIM) approach, which can be used to reflect the psychological behavior of the decision-maker. The Fermatean fuzzy cumulative prospect theory-TODIM approach is proposed in this paper to handle the problem of group decision-making. Using the entropy weight method with Fermatean fuzzy sets to obtain attribute weight information simultaneously improves rationality. This article applies the mentioned method to the risk assessment of breast cancer. It illustrates the risk assessment model based on the proposed method, concentrating on hot topics in contemporary culture.

Interval-value Pythagorean Fuzzy Prioritized Aggregation Operators for Selecting an Eco-Friendly Transportation Mode Selection

When selecting an environmentally friendly mode of transportation, many factors must be taken into account, such as capacity, delivery time, cost-effectiveness, and environmental impact. This is a difficult decision-making problem since it calls for the simultaneous evaluation and prioritization of several criteria. A thorough and methodical approach is required to make an informed decision that satisfies particular transportation needs while adhering to sustainability goals. The process entails balancing various trade-offs to determine the most environmentally friendly mode of transportation. When solving multi-attribute group decision-making (MAGDM) problems, prioritization is essential. Prioritization in fuzzy systems has been implemented through a variety of techniques and approaches. This paper tackles the MAGDM problem within the Pythagorean fuzzy (PyF) framework, taking into account the different requirements for experts and characteristics. We present new Aczel Alsina aggregation operators (AOs), whose efficient handling of uncertainties makes a major contribution to fuzzy mathematics. We suggest several PyF AOs, such as the PyF-prioritized Aczel Alsina geometric (PyFPAAG) and PyF-prioritized Aczel Alsina averaging (PyFPAAA), which are based on the Aczel Alsina t-norm and t-conorm. We show these AOs satisfy the aggregation criteria by examining their monotonicity, boundedness, and idempotency properties. The prioritization weights are derived from expert knowledge, enabling the suggested operators to capture the prioritization phenomenon among the aggregated arguments. Using a MAGDM technique, the proposed AOs are used to evaluate eco-friendly transportation modes. Their significance is confirmed by contrasting them with other well-known AOs.

Stochastic consensus for uncertain multiple attribute group decision-making problem in belief distribution environment

In the realm of uncertain multiple attribute group decision-making (MAGDM) problems, existing research often focuses on the development of consensus-enhancing algorithms grounded in optimization models. However, this paper takes a stochastic perspective, thoroughly considering the impact of uncertainty on decision-making. And a novel stochastic method to model group consensus is introduced with the listed three components: (1) the concept of stochastic rank analysis based on stochastic belief distribution (BD) is given to measure the uncertainty degree in the original BD matrix, which is then used to assign weights to decision makers (DMs). (2) in uncertain environments, to ensure the effectiveness of consensus from a probabilistic perspective, the stochastic consensus index is proposed by taking both the advantages of the Jensen-Shannon (JS) distance and the hesitant distance between stochastic BDs. Then, the expected acceptable group consensus index is further provided to measure the consensus of original preferences among the group, and (3) finally, to deal with the issue of no consensus information, an optimization model is constructed aimed at achieving an acceptable consensus that can generate recommendation advice for DMs, facilitating the attainment of a consensus. The effectiveness of the proposed method is exemplified through two case studies: purchase of new energy vehicles (NEVs) and a postgraduate interview scenario. Furthermore, sensitivity analysis and comparative analysis are presented to better prove its advantages.

Multi-attribute group decision-making method using single-valued neutrosophic credibility numbers with fairly variable extended power average operators and GRA-MARCOS

Data trading platform (DTP) selection is a classic multi-attribute group decision-making (MAGDM) problem. As an extension of intuitionistic fuzzy sets (IFSs), single-valued neutrosophic credibility numbers (SvNCNs) can express both fuzzy evaluation information and the credibility level of the information, offering better expressiveness in describing fuzzy decision-making information. However, existing studies on aggregation operators and decision-making methods in the SvNCN environment are inadequate. Therefore, this paper proposes a MAGDM technique based on the fairly weighted variable extended power average (SvNCNFWVEPA) operators of SvNCNs and grey relational analysis (GRA)-Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method. The main contributions are as follows: (1) we propose fairly operation rules for aggregating SvNCNs in an unbiased manner; (2) addressing the lack of SvNCN measurement research, we introduce preference distance and entropy measures for SvNCNs, then utilize the entropy measure to compute the objective weights of the attributes; (3) to effectively aggregate SvNCN information, inspired by the variable power geometric (VPG) operators and the extended power average (EPA) operators, we propose the variable extended power average (VEPA) operator for scientifically handling extreme values, extending it to SvNCNs with the SvNCNs fairly variable extended power average (SvNCNFVEPA) operators and their extended form; (4) we introduce the GRA method to calculate the degree of utility of alternatives relative to ideal and anti-ideal alternatives, forming the GRA-MARCOS method. This method can reflect both indicator differences and the similarity of alternatives, thereby rendering the evaluation results more scientific and objective; (5) to illustrate the application of the method to the MAGDM problem, we apply it to the example of DTP selection. Parameter sensitivity analysis and comparative analysis with other existing methods demonstrate that our proposed method is more scientific and flexible.

Comprehensive Analysis Using Probabilistic Linguistic Group Decision-Making and MEREC Technique With Sustainable Development Evaluation in Higher Education

University administrators should organize and coordinate various affairs, scientifically utilize resources such as budget, teaching equipment, and faculty, continuously improve teaching quality, and ensure the sustainable development of educational management. The sustainable development evaluation in higher education management is a multiple-attribute group decision-making (MAGDM) problem. Recently, the Exponential TODIM (ExpTODIM) and PROMETHEE technique was employed to put forward the MAGDM issues. The probabilistic linguistic term sets (PLTSs) are employed as a technique for characterizing uncertain information during the sustainable development evaluation in higher education management. In this paper, the probabilistic linguistic ExpTODIM-MABAC (PL-ExpTODIM-MABAC) technique is constructed to put forward the MAGDM under PLTSs. The MEREC technique is employed to obtain the weight values under PLTSs. Finally, a numerical example for sustainable development evaluation in higher education management is put forward to validate the ExpTODIM-MABAC technique.

Innovative ExpTODIM algorithm for interval-valued Pythagorean fuzzy information and new-media information communication influence evaluation

Undoubtedly, the Internet is an indispensable part of daily life in this era. In this context, the emergence of new media has played a disruptive role in information dissemination, and has also provided new definitions and evaluation standards for the impact of information dissemination. In an environment where excellent research is constantly emerging, it is of research value to redefine the elements of new media information dissemination from a new perspective and provide specific examples to analyze their information dissemination paths. The new-media information communication influence evaluation from super intellectual property (IP) perspective is a multipleattribute group decisionmaking (MAGDM) problem. Recently, the Exponential TODIM (an acronym in Portuguese of interactive and multicriteria decision making) method has been used to cope with MAGDM issues. The interval-valued Pythagorean fuzzy sets (IVPFSs) are used as a tool for characterizing uncertain information during the new-media information communication influence evaluation from super intellectual property. In this manuscript, the interval-valued Pythagorean fuzzy Exponential TODIM (ExpTODIM) (IVPFExpTODIM) method is built to solve the MAGDM under IVPFSs. In the end, a numerical case study for new-media information communication influence evaluation from super intellectual property is given to validate the proposed method. The main research contribution of this paper is constructed: (1) the Exponential TODIM (ExpTODIM) method is extended to the IVPFSs; (2) the interval-valued Pythagorean fuzzy Exponential TODIM (ExpTODIM) (IVPF-ExpTODIM) method is built to solve the MAGDM under IVPFSs; (3) the information Entropy is used to obtain the weight values based on the score value and accuracy value under IVPFSs; (4) Finally, a numerical case study for new-media information communication influence evaluation from super intellectual property is given to validate the proposed method.

Enhanced EDAS technique for quality evaluation of online games interface design based on three types of probabilistic linguistic similarity measures

The highest level of interface design for online games is to achieve invisibility, which means that the interface is very natural and easy to use. Players are proficient in using it and fully immerse themselves in the world of online games. It seems that the interface is transparent and they no longer feel its existence. Excellent online game interface design is the requirement of users that runs through the entire design process. But this is not to say that the usability of online game software can surpass other factors. All great designs seek a balance and harmony between artistic beauty, reliability, security, usability, cost, and performance. The quality evaluation of online games interface design is a multiple-attribute group decision-making (MAGDM) problem. Recently, the Evaluation Based on Distance from Average Solution (EDAS) technique, cosine similarity measure (CSM), Dice similarity measure (DSM), Jaccard similarity measure (JSM) and entropy technique has been separately employed to cope with MAGDM issues. The probabilistic linguistic term sets (PLTSs) are employed as a tool for conveying uncertain information during the quality evaluation of online games interface design. In this paper, the EDAS technique is expanded to the PLTSs and the probabilistic linguistic EDAS (PL-EDAS) technique based on cosine similarity measure (CSM), Dice similarity measure (DSM) and Jaccard similarity measure (JSM) is constructed to manage MAGDM issue. The information entropy technique is employed to implement the weight values based on CSM technique, DSM technique and JSM technique under PLTSs. Finally, the quality evaluation of online games interface design is employed to demonstrate the PL-EDAS technique and some comparative analysis is employed to demonstrate the PL-EDAS technique. Thus, the main research contribution of this work is constructed: (1) the information entropy technique is constructed to implement the attribute weight values based on CSM technique, DSM technique and JSM technique; (2) the PL-EDAS technique is constructed under PLTSs based on CSM technique, DSM technique and JSM technique; (3) an example for quality evaluation of online games interface design is employed to verify PL-EDAS technique and several decision comparative analysis is employed to verify the PL-EDAS technique.

Interval-valued q-rung orthopair fuzzy complex proportional assessment-based approach and its application for evaluating the factors of blockchain technology in various domains

Blockchain technology (BT) is a digitally decentralized, distributed and public ledger, which considers a secure and viable solution for storing and accessing the record transactions in a public or private peer-to-peer network and assures a smart world of automation of complex services. This paper aims to evaluate the factors persuading the BT adoption and also to assess the possible application areas for the adoption of BT. Implementation of the BT process considers multiple criteria and alternatives with uncertain information; therefore, it can be considered as an uncertain multi-criteria decision-making problem. In this context, a hybrid approach is proposed based on the criteria interaction through inter-criteria correlation (CRITIC) and the complex proportional assessment (COPRAS) methods with interval-valued q-rung orthopair fuzzy set (IVq-ROFS) named as the “IVq-ROF-CRITICCOPRAS” framework. In this approach, the significance values of the factors are computed through novel score function-based CRITIC model, whereas the CRITIC-based COPRAS model is employed to rank different domains/application areas for the adoption of BT under IVq-ROFSs environment. In this regard, a novel score function is introduced with its desirable characteristics. Further, the developed model is executed on a case study of BT adoption considering 14 factors, which confirms the efficiency and applicability of introduced approach. Based on the results, the healthcare BT alternative is most appropriate among the others. To evaluate its permanence, sensitivity analysis with regard to various ratings of decision strategic coefficient is presented under IVq-ROFS context. The strength of the developed approach is emphasized by comparing it with some of the former models under the context of IVq-ROFSs. The proposed extension of the COPRAS approach can be utilized to solve other complex group decision-making problems with uncertainties.

Group decision-making with hesitant fuzzy linguistic preference relations in view of worst and average indexes

To address the situation where Multi-criteria group decision-making (MCGDM) problems with hesitant fuzzy linguistic preference relations (HFLPRs), this study introduces a group decision-making (GDM) method in view of the worst additive consistency index (WACI) and average additive consistency index (AACI) simultaneously. First, several optimization models are constructed for deriving the WACI and AACI. The main characteristic of the constructed optimization models is that it takes into accounted the personalized individual semantics (PISs). And then the concept of acceptable additive consistent HFLPRs is developed based on the proposed WACI and AACI. Second, several optimization models are constructed for improving the consistency of HFLPRs. Two predefined thresholds for the WACI and AACI are considered in the proposed optimization models. It requires the consistency levels of all the linguistic preference relations (LPRs) associated with HFLPR meet the threshold of WACI, and the average consistency level of all LPRs reaches the threshold of AACI. Third, an algorithm is designed for deriving priority weights from acceptable consistent HFLPRs. Finally, the presented models are validated using a numerical example and extensive comparative analyses.

A Decision-Making Model with Cloud Model, Z-Numbers, and Interval-Valued Linguistic Neutrosophic Sets.

Interval-valued linguistic neutrosophic sets (IVLNSs), Z-numbers, and the trapezium cloud model are powerful tools for expressing uncertainty and randomness. This paper aims to combine these methodologies. First, we review relevant concepts and operators, introducing a novel combination of IVLNSs and Z-numbers, which establishes a new form of expression. Subsequently, we propose the Z-interval-valued linguistic neutrosophic set-trapezium-trapezium cloud (Z-IVLNS-TTC) model, designed to minimize information loss and distortion during quantification. A novel method for calculating the objective weight vector is then developed using multi-objective programming (MOP). Drawing inspiration from the TOPSIS method, we propose a new approach for calculating the distance between Z-IVLNS-TTCs based on the p-norm. Finally, a group decision-making problem is presented to demonstrate the practical application of the proposed method. To validate the effectiveness and feasibility of the method, sensitivity analysis and comparisons with existing approaches are conducted.

A data-driven minimum cost consensus model for group decision making with personality traits prediction

The minimum cost consensus model (MCCM) proposes an effective method for reaching group consensus in group decision-making problems. Conventional MCCM and its advanced models focus on the different behaviors and psychologies of decision-makers, but, it ignores the heterogeneity of decision-makers that activated them. Therefore, they need to assume the compromise limits and unit adjustment costs of decision-makers, which may be difficult to achieve in practice. To resolve this problem, this study will propose a novel data-driven minimum cost consensus model of different compromise limits and unit costs based on online Big Five personality traits prediction. First, this study uses the Convolutional Neural Network (CNN) and Bi-directional Long-Short Term Memory model (BiLSTM) to obtain the decision-maker's probability of agreeableness based on their Weibo online reviews. Second, a novel minimum cost consensus model considering the decision-maker's personality traits (MCCM-P) is established. To do that, the unit adjustment cost and the personalized compromise limits of decision-makers and their interrelations are defined based on the personality traits prediction. Finally, the MCCM-P is applied in a real group decision-making case study of a university student club activity selection. The result and comparative analysis show that the proposed MCCM model can obtain lower consensus reaching costs than the traditional method.

Similarity Measures of Probabilistic Interval Preference Ordering Sets and Their Applications in Decision-Making

The concept of probabilistic interval preference ordering sets (PIPOSs) provides a scientific and intuitive framework for solving real-life multi-criteria group decision-making problems. In some areas such as investment decision-making and supplier selection, PIPOSs have a wider application space, and the development of similarity and distance measures based on PIPOSs holds great significance. Similarity measure is a basic and prominent tool for dealing with imperfect and ambiguous information in fuzzy sets, but it can also be used to deal with uncertain information in preference ordering. These metrics play an important role in the actual decision-making process, as they effectively quantify the degree of similarity between two PIPOSs, and further allow for the prioritization of different scenarios. In this article, we sort out the definitions and arithmetic rules of PIPOSs, and creatively propose several new similarity measures based on PIPOSs. Then, we propose a group decision-making method based on similarity measures and conduct a comparative study with three existing similarity measures to illustrate its advantages over existing metrics. Finally, we confirm its validity through numerical illustrations in the case study, and also conduct a comparative assessment to verify the scientific validity and effectiveness of the newly introduced measure against the existing metrics.

A novel LqROPF-Entropy-WASPAS group model based on Frank aggregation operators and improved score function in linguistic q-rung orthopair fuzzy framework

Decision analysis plays a crucial role in our everyday actions. Efficient decision-making models rely heavily on accurately representing human cognitive knowledge. The linguistic q-rung orthopair fuzzy sets (LqROPFSs) offer a versatile means of representing qualitative cognitive information by adapting the parameter q to different scenarios. This study presents a novel scoring function to rank linguistic q-rung orthopair fuzzy numbers (LqROPFNs) with greater precision compared to the current score function. Next, we present novel arithmetic/geometric aggregation operators (AOs) that utilize new Frank operational rules to combine a finite collection of LqROPFNs. The work also examines the several desirable characteristics and special cases of the provided AOs. Furthermore, a novel decision-making model called the LqROPF-Entropy-WASPAS model has been introduced to address the challenges of multiple attribute group decision-making (MAGDM) problems in a linguistic q-rung orthopair fuzzy environment. The model incorporates proposed AOs and a scoring function. The suggested methodology is exemplified by considering a practical decision to select an online teaching platform. The validity of the results is confirmed through an extensive sensitivity analysis and comparative investigation employing various existing MAGDM approaches within the linguistic q-rung orthopair fuzzy framework. The proposed approach offers enhanced flexibility to decision experts, empowering them to analyze decision outcomes across diverse scenarios. This flexibility is achieved by allowing the manipulation of values associated with various parameters, enabling decision-makers to tailor the analysis according to their specific attitudes and requirements. This adaptability ensures a more advanced and personalized analysis of decision outcomes, accommodating decision experts’ distinct viewpoints and preferences in varying situations.

A large-scale group decision-making framework based on two-dimensional picture fuzzy sets in the selection of optimal carbon emission reduction alternatives

This article proposes the concept of two-dimensional picture fuzzy sets (TDPFS). This new framework extends the original picture fuzzy sets (PFS) by incorporating a confidence level to evaluate expert opinions using probability. This innovation facilitates the stratification and dimensionality reduction of large-scale expert groups, while also offering a new perspective for examining group decision-making problems. This article determines the weights of each subgroup by considering the confidence level of expert evaluation, the cohesion of the subgroups, and the size of the subgroups. This approach simplifies the decision-making process by transforming the originally complex large-scale group decision-making problem into a traditional decision-making problem. Finally, the traditional consensus reaching process (CRP) ensures that experts meet specific consensus requirements in decision-making. To verify the feasibility and practicality of the framework in practical applications, we applied it to the selection of carbon emission reduction paths and alternatives in China’s iron and steel industry. This practice not only demonstrates the theoretical value of our research results but also highlights its potential in practical operations.

Neutrality geometric aggregation operators based on cubic q-rung orthopair fuzzy sets and their applications in multiple attribute group decision-making problems

Cubic q-rung orthopair fuzzy set (Cq-ROFS) is a hybrid set that can hold much more information to handle data uncertainties. It provides more space for decision makers (DMs) to describe their opinion in the multi-attribute group decision-making (MAGDM). In order to involve neutral character to discuss the neutral values assigned by decision-makers, in this paper, some cubic q-rung orthopair fuzzy aggregation operators based on neutrality operation are proposed, such as the cubic q-rung orthopair fuzzy weighted neutrality geometric (Cq-ROFWNG) operator, the cubic q-rung orthopair fuzzy ordered weighted neutrality geometric (Cq-ROFOWNG) operator, the cubic q-rung orthopair fuzzy hybrid neutrality geometric (Cq-ROFHNG) operator. Meanwhile, some properties of these operators are discussed. Further, we develop a novel MAGDM approach based on the proposed operators in a cubic q-rung orthopair fuzzy environment. Finally, numerical examples are provided to demonstrate the effectiveness and superiority of the proposed method through a detailed comparison with existing methods.

Time-series bidirectional adjustable N-soft expert MABAC method and its application for multi-attribute group decision-making

In hybrid models of soft expert sets, experts express only agreed or disagreed opinions about existing grades. This paper proposes a time-series bidirectional adjustable N-soft expert set model to address the shortcomings of existing models that cannot adjust existing grades to a more reasonable state or describe decision problems involving different times. Firstly, this model can explain the experts’ uncertain opinions and make positive or negative adjustments about existing grades. Secondly, the model contains information about time, describes dynamic multi-attribute group decision-making problems and explores objects’ changes and developments over time. And some related operations and propositions are derived. In addition, a new method called the bidirectional adjustable N-soft expert MABAC (multi-attributive border approximation area comparison) is proposed. On the one hand, the proposed method uses deviation maximizing and exponential decay methods to determine the time weights, ensuring the reliability of the time weights. On the other hand, it ranks objects based on their distances from an approximate boundary region, limiting the unconditional compensation among attribute values. Finally, this paper presents an example to verify its effectiveness and reliability by results analysis, sensitivity analysis, and comparison analysis.

New covering techniques and applications utilizing multigranulation fuzzy rough sets

In order to conduct an in-depth study of Zhan’s methodology pertaining to the covering of multigranulation fuzzy rough sets (CMG\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {C}_{{MG}}$$\\end{document}FRSs), we build two families: the family of fuzzy β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta $$\\end{document}-minimum descriptions and the family of β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta $$\\end{document}-maximum descriptions. Subsequently, utilizing these notions, we proceed to develop two variations of covering via optimistic (pessimistic) multigranuation rough set samples (CO(P)MG\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {CO(P)}_{{MG}}$$\\end{document}FRS). The axiomatic properties are examined. In this study, we examine four models of covering using variable precision multigranulation fuzzy rough sets (CVPMG\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {CVP}_{{MG}}$$\\end{document}FRSs). We proceed with analyzing the features of these models. Interconnections between these planned plans are also elucidated. This study explores algorithms that aim to identify innovative strategies for addressing multiattribute group decision-making problems (MAGDM) and multicriteria group decision-making problems (MCGDM). The test examples have been elucidated to provide an inclusive grasp of the efficacy of the offered samples. Ultimately, the distinctions between our methodologies and the preexisting research have been demonstrated.

Enhanced minimum cost consensus model for interval type-2 fuzzy social network group decision making focusing on individual attributes and group attitude

In group decision-making scenarios, consensus reaching is a crucial factor for resolving conflicts of opinion among groups, and social network analysis plays a significant role in fostering group consensus. This paper constructs a social network-driven minimum cost consensus framework for interval type-2 fuzzy group decision-making problems involving different individual attributes. Firstly, this paper proposes a theoretical social network analysis by the implementation of propagation efficiency, propagation reliability and opinion similarity to generate comprehensive trust relationships. The aim is to obtain missing trust relationships and individual centrality. Secondly, a minimum cost consensus model is constructed to give recommendation advice for identified inconsistent decision-makers according to their adjustment willingness. The novelty of the model lies in its capability to consider decision-makers’ individual attributes and group attitude or behavior. Then, this paper proposes an interval type-2 fuzzy Alternative by Alternative Comparison (ABAC) method for ranking multiple alternatives which address the rank reversal problem. Lastly, a case study on the selection of alternative charging point operators illustrates the effectiveness of the proposed method, and comparison and sensitivity analysis show the advantages of the proposed method.

Interval-valued fermatean fuzzy Aczel-Alsina geometric aggregation operators and their applications to group decision-making

Developing new aggregation operators on various classes of fuzzy sets and their generalizations is important in modelling real-life decision-making problems. Interval-valued Fermatean fuzzy sets (IVFFs) generalize the idea of interval-valued Pythagorean fuzzy sets (IVPFS) play a crucial role in modelling problems involving inadequate information. Decision-making problems modelled using IVFFNs require different score functions and aggregation operators on the set of IVFFNs. This study mainly focuses on establishing a few interval-valued Fermatean fuzzy (IVFF) aggregation operators by integrating the Aczel-Alsina (AA) operations to deal with group decision-making (GDM) problems. In this work, first, we discuss various Aczel-Alsina-based IVFF operations such as AA sum, AA product, and AA scalar multiplication for proposing a few new aggregation operators for the IVFF environment based on the new IVFF operations. Secondly, we introduce a few operators, including the interval-valued Fermatean fuzzy Aczel-Alsina (IVFFAA) weighted geometric operator, the IVFFAA ordered weighted geometric (IVFFAAOWG) operator, and the IVFFAA hybrid geometric (IVFFAAHG) operator. Various important properties such as idempotency, boundness, and monotonicity have also been studied. Thirdly, we establish multi-criteria group decision-making (MCGDM) method for solving real-life decision-making problems. Fourthly, we solve a model GDM problem to show the applicability and efficacy of our proposed MCGDM method, which utilizes the IVFFAAWG operator. Further, a sensitivity analysis is performed to ensure better performance, and finally, a comparative study of our method is done by comparing our proposed MCGDM approach with different existing methods.

Complex Fermatean fuzzy geometric aggregation operators and their application on group decision-making problem based on Einstein T-norm and T-conorm

Complex Fermatean fuzzy geometric aggregation operators and their application on group decision-making problem based on Einstein T-norm and T-conorm