Multiple Attribute Group Decision Research Articles

MEREC-Based Combined Framework for Multiple-Attribute Group Decision-Making in Probabilistic Linguistic Environment

The deployment of our country's innovation driven strategy has been implemented, and transforming the economic development mode has become an important content. It is particularly important to use finance to serve technological innovation and promote economic development. The efficiency evaluation of technology finance development is multiple-attribute group decision-making (MAGDM). Presently, the utilization of Logarithmic TODIM (LogTODIM) and CoCoSo approach was implemented to address the MAGDM challenges. To effectively evaluate the development of technology finance, the application of probabilistic linguistic term sets (PLTSs) has been illustrated as a method to characterize uncertain information. This research introduces the probabilistic linguistic LogTODIM-CoCoSo (PL-LogTODIM-CoCoSo) approach to address MAGDM concerns within PLTSs. Additionally, the MEREC approach is illustrated to handle the weight assignment process under PLTSs. Finally, numerical example for efficiency evaluation of technology finance development is put forward to validate the LogTODIM-CoCoSo approach.

Multiple attribute group decision making based on $$p,q$$-quasirung orthopair Bonferroni mean operators and their applications

Multiple attribute group decision making based on $$p,q$$-quasirung orthopair Bonferroni mean operators and their applications

Selection of Internet of Things-enabled sustainable real-time monitoring strategies for manufacturing processes using a disc spherical fuzzy Schweizer–Sklar aggregation model

The emergence of the Internet of Things (IoT) for monitoring in real-time is geared towards sustainable energy consumption practices by taking control over energy loss. The promising potential of current IoT real-time monitoring systems paves the way for future developments in monitoring devices with eco-friendly sensing capabilities. As a result, the creation of effective IoT real-time monitoring devices targeted at decreasing energy loss becomes crucial. This modeling procedure falls under the realm of multiple-attribute group decision-making (MAGDM), aiming to integrate the Schweizer–Sklar (SS) τ-norm and τ-conorm within the disc spherical fuzzy (D-SF) framework. The objective is to enhance the flexibility of D-SF in dealing with intricate and uncertain data. The core focus of this research is on deriving SS τ-norm and τ-conorm for D-SF data, consequently introducing innovative aggregation operators. The article offers the fundamental D-SF operations using SS aggregation operators in a systematic manner, with thorough theorem justifications. A new MAGDM tool is presented, created simply to manage ambiguous and imprecise data utilizing the suggested operators. Our model is specifically designed to tackle the critical issue of reducing energy loss in IoT real-time monitoring systems. The research not only focuses on model accuracy but also emphasizes its effectiveness in solving this pressing problem, demonstrating significant advancements in sustainable energy practices. Moreover, the proposed aggregation operators are subjected to a comparative analysis. This comprehensive comparison not only enhances the operators’ efficacy but also underscores their relevance in real-world decision-making scenarios.

Enhanced GDM Algorithms for College Mathematics Teaching Quality Evaluation Under Interval-Valued Intuitionistic Fuzzy Sets

The college mathematics teaching quality evaluation is the multiple-attribute group decision-making (MAGDM). Currently, the Exponential TODIM (ExpTODIM) and MABAC was executed to put forward MAGDM. The interval-valued intuitionistic fuzzy sets (IVIFSs) are executed for portraying fuzzy data during the college mathematics teaching quality evaluation. In this study, the interval-valued intuitionistic fuzzy number ExpTODIM-MABAC (IVIFN-ExpTODIM-MABAC) approach is put forward the MAGDM with IVIFSs. At last, numerical example for college mathematics teaching quality evaluation is executed to verify the IVIFN-ExpTODIM-MABAC. The major contributions are executed: (1) the ExpTODIM-MABAC based was extended to IVIFSs along with Entropy model; (2) the entropy is employed to execute the weight under IVIFSs. (3) the IVIFN-ExpTODIM-MABAC approach is put forward MAGDM with IVIFSs; (4) numerical example for college mathematics teaching quality evaluation and comparative analysis is executed to proof the IVIFN-ExpTODIM-MABAC approach.

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.

A hybrid novel SWARA-ELECTRE-I method using probabilistic uncertain linguistic information for feature selection in image recognition

In the digital age, the exponential growth of data poses significant challenges for analysts and machine learning algorithms in pattern detection due to its high dimensionality. This study addresses the dimensionality problem by leveraging Probabilistic Uncertain Linguistic Term Set (PULTS), which combine Uncertain Linguistic Term Set (ULTS) with associated probabilities to handle uncertainty in decision-making. We introduce the PUL-weighted average operator to integrate the opinions of multiple decision-makers and propose a novel ELimination and Choice Translating REality (ELECTRE-I) method for optimizing alternatives in multiple attribute group decision-making (MAGDM) scenarios. This method is enhanced by the Stepwise Weight Assessment Ratio Analysis (SWARA) method to determine the relative weight of each attribute. By integrating SWARA with the ELECTRE-I method, we develop a comprehensive approach to tackle MAGDM problems using PULTS. A numerical example involving feature selection in image recognition demonstrates the method’s effectiveness and accuracy. Comparative studies highlight the advantages of our approach in producing a small feature set with high classification accuracy. The proposed method offers a robust solution for feature selection in image recognition and other MAGDM problems, significantly improving decision-making accuracy and efficiency. The methodology’s simplicity and computational ease make it applicable across various domains requiring effective dimensionality reduction.

Incorporating q-Rung Picture Fuzzy Frank Prioritized Weighted Aggregators with Multimoora Strategy for Decision Making

The Frank norm operators and ˇq-rung picture fuzzy sets offer a versatile framework due to their adjustable parameters. Conversely, prioritized weighted operators are crucial for indicating the relative significance of alternatives and attributes. In this research, we have devised Frank’s prioritized weighted procedure for ˇq-rung picture fuzzy numbers. Thus, the ˇq-rung picture fuzzy prioritized weighted Frank averaging (ˇq-RPFPWFA) and geometric (ˇq-RPFPWFG) operators are presented and analyzed along with some of their properties. Utilizing these proposed operators, a novel approach is established, combining the enhanced MULTIMOORA (MM) procedure with unspecified weight data. A numerical example regarding the multiple attribute group decision making (MAGDM) problem of selecting the financial director of a residential society is handled based upon the aforementioned procedure. Furthermore, we have compared the results with those obtained using available operators. Evidently, the novel ˇq-RPFPWFA and ˇq-RPFPWFG aggregators with the MM technique yield reasonable and consistent outcomes for solving MAGDM.

Applications of Pythagorean Uncertainty weighted Average Aggregation operator to Multiple attribute group Decision Making

Applications of Pythagorean Uncertainty weighted Average Aggregation operator to Multiple attribute group Decision Making

Enhanced Cross-Entropy Exponential TODIM Framework for Interval-Valued Pythagorean Fuzzy Information and Applications to Art Teaching Quality Evaluation

Art teachers in preschool education need to possess not only high professional competence but also a commitment to continuous learning to enhance their comprehensive teaching abilities. The art teaching quality evaluation in preschool education programs at vocational colleges is multiple-attribute group decision-making (MAGDM). Currently, the Exponential TODIM and cross-entropy approach was employed to address MAGDM. Interval-valued Pythagorean fuzzy sets (IVPFSs) serve as a strategy for characterizing uncertain data during the art teaching quality evaluation in preschool education programs at vocational colleges. In this study, the interval-valued Pythagorean fuzzy number cross-entropy Exponential TODIM (IVPFN-CE-ExpTODIM) approach is developed to bring up MAGDM problems under IVPFSs. To validate the proposed method, a numerical study is conducted on the art teaching quality evaluation in preschool education programs at vocational colleges.

Novel Linguistic Intuitionistic Fuzzy Score and Accuracy functions for Linguistic Intuitionistic Fuzzy TOPSIS Method

Abstract: This work proposes various Linguistic Intuitionistic Fuzzy Score and Accuracy functions (LIF-Score and LIFAccuracy), including linguistic degree, membership, non-membership, and hesitancy degree, for Linguistic Intuitionistic Fuzzy Sets (LIFSs) in Multiple Attribute Group Decision Making (MAGDM) problems. The proposed functions are integrated into the Linguistic Intuitionistic Fuzzy-Technique of Order Preference by Similarity to Ideal Solution (LIF-TOPSIS) MAGDM approach to rank the alternatives, and a numerical example demonstrating their use in ranking alternatives is shown in the paper.

An integrated CRITIC-MABAC model under 2-tuple linguistic cubic q-rung orthopair fuzzy information with advanced aggregation operators, designed for multiple attribute group decision-making

An integrated CRITIC-MABAC model under 2-tuple linguistic cubic q-rung orthopair fuzzy information with advanced aggregation operators, designed for multiple attribute group decision-making

Application of Improved Class of Weighted Averaging Operators in Intuitionistic Fuzzy Ann Guided MAGDM E-Commerce Problems

Objectives: To propose a novel and improvised Artificial Neural Network (ANN) technique to solve Multiple Attribute Group Decision Making (MAGDM) problems and an improved class of Aggregation operators for combining Intuitionistic Fuzzy Set (IFS) matrices for the ANN algorithm. Methods: A novel class of improved aggregation operators namely the Improved Intuitionistic Fuzzy Weighted Arithmetic Averaging (IM-IFWAA) operator and the Improved Intuitionistic Fuzzy Ordered Weighted Averaging (IM-IFOWA) operator are proposed in this work. The proposed improved class of aggregation operators will aggregate the IFS matrix data sets appearing in the form of matrices and then the revised input vectors which is then fed into the ANN algorithm following Delta, Perceptron, and Hebb Learning Rule for the next phase. Findings: Aggregating the Intuitionistic Fuzzy set information or data in today’s digital world is a tedious task in the field of MAGDM problem-solving. In this work, two new classes of operators for aggregating the data sets are proposed namely the IM-IFWAA operator and the IM-IFOWA operator which will improve the performance of the ANN. Necessary theorems for the proposed operators are proved to provide consistency of the same. The input created using the aforementioned operators is then fed into the novel ANN algorithm for further computations. Varieties of learning rules are then engaged for ranking of the best alternative for the decision problem. The developed theory is supported by a numerical example which is computed using all the proposed techniques. Novelty: Most of the research done on Intuitionistic Fuzzy Artificial Neural Network models are based on learning rules or using some other calculations. The proposed methods of using novel and improvised aggregation operators for ANN are used to find the inputs for ANN where varieties of learning rules for ANN are employed for effective decision analysis. Keywords: ANN, Learning Rules, MAGDM, Intuitionistic Fuzzy Sets, Weighted Aggregation Operator

Research on the evaluation method of whole process engineering consulting service modes based on uncertain multiple-attribute group decision-making

With the continuous development of the economy and society, the engineering construction industry has experienced rapid growth, and higher requirements have been put forward for engineering consulting services. Currently, most engineering consulting service enterprises in China only provide relevant consulting services for engineering construction at a certain stage, lacking overall planning and overall control over the entire lifecycle of engineering construction. In order to improve the scientific level of fixed assets investment decision-making and construction management, it is of great value to explore professional engineering consulting services covering the whole process of project construction in the project decision-making stage and implementation stage to achieve the optimization of project quality and economic benefits. The whole process engineering consulting service modes (ECSM) evaluation is a multiple-attribute group decision-making (MAGDM). Recently, the TODIM technique and MACONT technique was created to put forward MAGDM. The interval neutrosophic sets (INSs) are created as a tool for managing fuzzy information during the whole process ECSM evaluation. In this work, the interval neutrosophic number TODIM-MACONT (INN-TODIM-MACONT) technique is created to put forward the MAGDM under INSs. Finally, a numerical analysis for whole process ECSM evaluation is given to validate the proposed technique. The main contributions are outlined: (1) the TODIM and MACONT technique was enhanced under INSs; (2) Information Entropy is created to conduct the weight information under INSs. (3) the INN-TODIM-MACONT technique is created to implement the MAGDM under INSs; (4) a numerical example for whole process ECSM evaluation and some comparative analysis is created to verify the INN-TODIM-MACONT technique.

Prioritization of sustainable development plannings for supply chain finance using a novel Triangular neutrosophic-based TOPSIS-CPT-VIKOR model

The development of sustainable supply chain finance (SSCF) promotes the improvement of the long-term production and operational performance of enterprises. It is significant to explore both environmentally friendly and cost-effective solutions, especially in the presence of uncertain information. The triangular neutrosophic number (TrNN) can enable decision-makers (DMs) to express their preferences in high uncertainty. This paper aims to provide a systematic multiple-attribute group decision-making (MAGDM) framework to support DMs in prioritizing sustainable development plannings by combining the best-worst method (BWM), optimization model of maximizing deviation method, and uniformity entropy theory. Moreover, a TrNN-based extended TOPSIS method is developed to measure the differences in DMs’ importance. We incorporate the cumulative prospect theory (CPT) method to account for DMs’ psychological behaviors in risk scenarios. The CPT-VIKOR methodology is employed to rank alternatives, enhancing the precision of decision-making. Finally, the proposed approach is successfully applied to the LNG industry to illustrate its applicability.

TODIM–VIKOR framework for development potential evaluation of forest health tourism based on the single-valued neutrosophic number multiple attribute group decision making

TODIM–VIKOR framework for development potential evaluation of forest health tourism based on the single-valued neutrosophic number multiple attribute group decision making

Enhanced decision technique for optimized crude oil pretreatment under disc spherical fuzzy Aczel Alsina aggregation information

Crude oil, the backbone of modern industry, holds unparalleled significance as a global energy cornerstone. Unlocking its potential hinges on effective pretreatment techniques, ensuring purity, and maximizing efficiency. This study extends the established Spherical Fuzzy Set paradigm to explore the domain of Disc Spherical Fuzzy Sets (D-SFSs) in critical decision-making for crude oil preparation. Investigating D-SFSs within the Aczel Alsina norm, the research employs comparison rules, conversion rules, and distance metrics. Primary operations of the Aczel Alsina norm in D-SFSs are examined, laying the groundwork for introducing unique aggregation operations within this framework. The paper’s primary aim is to propose a hybrid method, termed MEREC-SWARA-MARCOS-D-SFSs Multiple Attribute Group Decision Making, which integrates the aforementioned aggregation procedures. A case study on crude oil pretreatment validates the effectiveness of the proposed method. Furthermore, a comprehensive comparison with CoCoSo underscores the reliability of the method. This study represents a significant stride in enhancing decision-making by providing a robust framework to tackle complex situations, particularly in the critical domain of crude oil pretreatment.

A multiple attribute group decision making model based on 2-tuple linguistic pythagorean fuzzy dombi aggregation operators for optimal selection of potential global suppliers

Multiple-Attribute Group Decision-Making (MAGDM) is a significant area of research in decision-making, and its principles and methodologies are widely implemented. A Pythagorean Fuzzy Set (PFS) is an extension of an Intuitionistic Fuzzy Set (IFS) that is highly valuable for representing uncertain information in real-world scenarios. The 2-Tuple Linguistic Pythagorean Fuzzy Number (2TLPFN) is a specific type of Pythagorean Fuzzy Number (PFN) that can be used to represent uncertainty in real-world decision making through the use of 2-Tuple Linguistic Terms (2TLTs). This paper focuses on the examination of Multiple Attribute Group Decision Making (MAGDM) using 2TLPFNs. Dombi's t-norm and t-conorm operations were commonly referred to as Dombi operations, which might have been greater degree of applicability if offered in a new form of flexibility within the general parameter. In this research, we implement Dombi operations to construct some 2-Tuple Linguistic Pythagorean Fuzzy (2TLPF) Dombi Aggregation operators. These operators include the 2TLPF Dombi Weighted Averaging (2TLPFDWA) operator, 2TLPF Dombi Ordered Weighted Averaging (2TLPFDOWA) operator, 2TLPF Dombi Weighted Geometric (2TLPFDWG) operator, and 2TLPF Dombi Ordered Weighted Geometric (2TLPFDOWA) operator. An analysis is conducted to examine the unique characteristics of these suggested operators. Subsequently, we leveraged the proposed operators to develop a model aimed at tackling the MAGDM problems in the 2TLPF environment. Eventually, a suitable instance has been demonstrated to validate the formation of the model as well as exhibit its implementation and resilience.

Projection-driven optimization framework for risk assessment of “dual carbon” audit in power grid enterprises with q-rung orthopair fuzzy MAGDM

Power grid enterprises are the backbone of promoting clean and low-carbon energy transformation, playing an important role in achieving carbon peak and carbon neutrality. It is very necessary to audit the implementation of the “dual carbon” work of power grid enterprises, in order to better implement the national “dual carbon” policy and serve the development of the national economy. The risk assessment of “dual carbon” audit in power grid enterprises is multiple-attribute group decision-making (MAGDM). In this study, in light with projection measure technique and bidirectional projection measure technique, four forms of projection measure technique with q-rung orthopair fuzzy sets (q-ROFSs) are conducted. Then, two weighed projection techniques are conducted to manage the MAGDM. Finally, a numerical example for risk assessment of “dual carbon” audit in power grid enterprises and comparative analysis is utilized to verify the developed techniques. The major contribution of this research is constructed: (1) entropy technique is implemented to determine the weight values in line with score number (SN) and accuracy number (AN); (2) two weighed projection techniques are implemented to put forward MAGDM with q-ROFSs; (3) the numerical example for risk assessment of “dual carbon” audit in power grid enterprises is implemented to show the two weighed projection techniques under q-ROFSs; and (4) comparative studies are constructed with existing techniques.

A generalized interval-valued p,q Rung orthopair fuzzy Maclaurin symmetric mean and modified regret theory based sustainable supplier selection method

A novel interval valued p,q Rung orthopair fuzzy (IVPQ-ROF) multiple attribute group decision making (MAGDM) method for sustainable supplier selection (SSS) is proposed in this paper. This study mainly contains two research points: (1) tackling the interrelation between attributes; and (2) describing the psychological state and risk attitude of decision makers (DMs). For the first research point, we introduce the Archimedean operation rules for interval valued p,q Rung orthopair fuzzy sets (IVPQ-ROFSs), then the generalized interval valued p, q Rung orthopair fuzzy Maclaurin symmetric mean (GIVPQ-ROFMSM) operator and the generalized interval valued p, q Rung orthopair fuzzy weighted Maclaurin symmetric mean (GIVPQ-ROFWMSM) operator are defined to reflect the correlation between attributes. For the second research point, we introduce the positive ideal degree (PID) and negative ideal degree (NID) based on projection of IVPQ-ROFSs, and modified regret theory. Both of them consider the best alternative and worst alternative, so as to reflect the psychological state and risk attitude of DMs. Finally, a SSS problem is presented to manifest the effectiveness of the designed method. We also provide sensitivity analysis and comparative analysis to further demonstrate the rationality and validity of the proposed method.

Intuitionistic Fuzzy Gram-Schmidt Orthogonalized Artificial Neural Network for Solving MAGDM Problems

Objectives: To propose a suitable decision-making model based on Intuitionistic Fuzzy sets (IFSs) and Gram-Schmidt orthogonalization process for Artificial Neural Network (ANN). Methods: The IFS data sets appearing in the form of matrices are aggregated using the available aggregation operators in the literature and then the collective aggregated information is processed through Gram-Schmidt orthogonalization for the revised input vectors which is then fed into the ANN algorithm following Delta Learning Rule for the next phase. The weight updation is performed through the ANN and the output is improvised. Findings: The proposed Gram-Scmidt Orthogonalization process is utilized in Intuitionistic Fuzzy Artificial Neural Network model. The Delta learning rule is utilized in the process of the Neural Network, where the Intuitionistic Fuzzy nature of the input data is transformed into a fuzzy data and then the ranking of the alternatives is done based on the weights updation through the learning phase of the ANN. Once the vector is trained out of the learning phase, it is then processed through the activation function for the final selection of the best alternative required of the Multiple Attribute Group Decision Making (MAGDM) problem posed in this work. To demonstrate the usefulness and applicability of this new method with the Gram-Schmidt process, the numerical example also adds more insight to the proposed methodology of ANN with the application of some Linear Space techniques. Novelty: Most of the research done on Intuitionistic Fuzzy Artificial Neural Network model are based on learning rules or using some other calculations. The proposed Gram-Schmidt Orthogonalization process is used to find the orthogonal basis that are used as input training vectors in the Delta learning rule for ANN. Keywords: MAGDM, ANN, Aggregation operators, Learning Rules, Intuitionistic Fuzzy sets, Gram-Scmidt Orthogonalization