Multi-attribute Group Decision Research Articles

The cross-border e-commerce platform selection based on the probabilistic dual hesitant fuzzy generalized dice similarity measures

Abstract Cross-border e-commerce platform (CBECP) plays a very important role in the development of a cross-border e-commerce (CBEC). How to select the best CBECP scientifically and reasonably is a very critical multi-attribute group decision-making (MAGDM) issue. With the uncertainty of people’s cognition of the objective world, the decision-making process is full of a lot of fuzzy information. In view of the great advantages of probabilistic dual hesitation fuzzy set (FS) in expressing decision-making information, and in combination with the very extensive use of the Dice similarity measure (DSM), a new MAGDM method is proposed for the optimal CBECP selection (CBECPS) under the probabilistic dual hesitation fuzzy (PDHF) environment. First, on the basis of reviewing a large number of documents on the CBECPS for CBEC, the evaluation index system for the CBECPS is constructed; second, several new DSMs are proposed in the PDHF environment; third, based on the two newly proposed probabilistic dual hesitant weighted generalized Dice similarity measures, two novel MAGDM methods are provided for CBECPS, which are used for CBECPS; finally, the two established MAGDM techniques are compared with the existing decision-making methods, and the parameter analysis is carried out to illustrate the effectiveness and superiority of the two established MAGDM techniques. The two established techniques can not only be used for CBECPS of CBEC, but also be extended to similar related research.

A novel MAGDM-based methodology with SVNSs and applications to collaborative innovation ability training mode selection of virtual teaching and research platforms

The virtual teaching and research platform is a cognitive and practical activity for relevant teachers engaged in education and teaching research to improve and improve the quality of education and teaching, relying on the unique functions and resource advantages of the network, using information technology and various network tools to carry out teaching, feedback, etc., and to carry out education and teaching exchange, discussion and other education and teaching research with other administrative organizations or teachers in space regions. It can break the constraints of time, space, organization and other factors. It is an effective form of educational and teaching activities carried out for common goals. It integrates teaching, guidance, discussion, exchange, resource sharing and other functions, and is an important part of the “double training plan”. The collaborative innovation ability training mode selection of virtual teaching and research platforms as a multi-attribute group decision making (MAGDM) problem. In this paper, the Evaluation Based on Distance from Average Solution (EDAS) method is extended to the single-valued neutrosophic sets (SVNSs) and single-valued neutrosophic EDAS method (SVN-EDAS) is proposed to solve MAGDM problem. Finally, the collaborative innovation ability training mode selection of virtual teaching and research platforms is given to show the SVN-EDAS model and some comparative analysis is conduct to depict the advantages of SVN-EDAS.

A probability-based fuzzy algorithm for multi-attribute decision-analysis with application to aviation disaster decision-making

This research addresses the challenges of multi-attribute group decision-making in a hesitant fuzzy environment, where randomness and uncertainty introduce biases into practical making choices. A probability-based double uncertain fuzzy (PDHF) algorithm is proposed to overcome these challenges. The algorithm constructs probability-based double hesitant fuzzy information matrices for each decision expert. The maximum deviation and entropy methods determine objective weights for decision experts and attribute indicators. Combining an improved scoring function and deviation function generates a comprehensive decision evaluation information matrix for decision experts. Subsequently, a decision algorithm that integrates probability-based double hesitant fuzzy sets with the preference ranking organization method (PROM) is employed to obtain the final decision result. As part of a case study, the algorithm is used to evaluate emergency action plans for aviation disaster accidents. Comparing the results with those from the TOPSIS, VIKOR, and PDHFS decision-making algorithms shows that the suggested PDHF algorithm works well and can be trusted for the multi-attribute group decision-making problems in hesitant fuzzy environments.

Supplier selection through interval type-2 trapezoidal fuzzy multi-attribute group decision-making method with logarithmic information measures

Supplier selection is a hot issue in logistics and procurement under the new economic situation. For the problems of supplier selection, the uncertainty and complexity of decision-making information often led to the mistakes in the process of group decision-making. In this paper, interval type-2 trapezoidal fuzzy (IT2TrF) information is used to deal with uncertainty and complex decision-making information, and a multi-attribute group decision making (MAGDM) method with IT2TrF information measures is designed for supplier selection problems. Specifically, we first present three axiomatic definitions for IT2TrF information measures. Then, based on logarithmic function, the IT2TrF logarithmic information entropy, the IT2TrF logarithmic similarity and the IT2TrF logarithmic cross-entropy are proposed to measure the fuzziness and similarity of decision-making information. In addition, an attribute weights determination method based on IT2TrF information entropy is established. Finally, a numerical example regarding assess the performance of suppliers is provided, and the comparative analysis demonstrates the applicability and feasibility of the developed MAGDM method.

A novel consensus reaching method for the preference-approval structure based on regret theory and its application in evaluating pension institutions

PurposeThe purpose of this paper is to propose a consensus method for multi-attribute group decision-making (MAGDM) problems based on preference-approval structure and regret theory, which can improve the efficiency of decision-making and promote the consensus level among individuals.Design/methodology/approachFirst, a new method to obtain the reference points based on regret theory and expert weighting method is proposed. Second, a consensus reaching method based on preference-approval structure is proposed. Then, an adjustment mechanism to further improve the consensus level between individuals is designed. Finally, an example of the assessment of elderly care institutions is used to illustrate the feasibility and effectiveness of the proposed method.FindingsThe feasibility and validity of the proposed method are verified by comparing with the advanced two-stage minimum adjustment method. The compared results show that the proposed method is more consistent with the actual situation.Research limitations/implicationsThis paper presents a consensus reaching method for MAGDM based on preference-approval structure, which considers the avoidance behaviors of individuals and reference points. Decision makers (DMs) can use this approach to rank and categorize alternatives while further increasing the level of consensus among them. This can further help determine the optimal alternative more efficiently.Originality/valueA new MAGDM problem based on the combination of regret theory and individual reference points is proposed. Besides, a new method of obtaining experts' weights and a consensus reaching method for MAGDM based on preference-approval structure are designed.

Multi-attribute group decision-making based on intuitionistic fuzzy aggregation operators defined by weighted geometric means

AbstractThis paper proposes a multi-attribute group decision-making methodology that takes advantage of a new weighted geometric mean aggregation operator on intuitionistic fuzzy numbers (IFNs). To this purpose, first, we define the intuitionistic fuzzy direct weighted geometric operator on IFNs, then we prove that it is a representable intuitionistic aggregation operator, and afterwards, we compare it with other aggregation operators motivated by the geometric mean. We use two proxies for the quantitative comparison of performances, namely the average of the Euclidean distances to the IFNs and the sum of squared error inspired by the k-means clustering algorithm.

A new approach to sentiment analysis algorithms: Extended SWARA-MABAC method with 2-tuple linguistic [formula omitted]-rung orthopair fuzzy information

Sentiment analysis is a process of computationally identifying and categorizing opinions expressed in a piece of text. It employs several algorithms including Bayesian, support vector machine, and naive Bayes. Such algorithms utilize text analysis and natural language processing to categorize words as positive, negative, or neutral. It needs decision-making techniques i.e. algorithms to process the words, which ultimately enable companies to obtain a comprehensive understanding of their customers’ perceptions of the brand. These techniques are mostly interlinked with computer science, and software that have vital roles in numerous decision-making and problem-solving processes. Natural language processing is one of these techniques which focused on endowing computers with the capacity to comprehend text and spoken language akin to human beings. It is often regarded as a common multi-attribute group decision-making (MAGDM) issue in the field of sentiment analysis. A scenario in which individuals simultaneously choose from the options presented to them is referred to as group decision-making. As a result, this article examines the theory of sentiment analysis algorithms and uses the Step-Wise Weight Assessing Ratio Analysis (SWARA) with Multi-Attributive Border Approximation Area Comparison (MABAC) to evaluate the intrinsic and global advantages, disadvantages, potential, and threats of different sentiment analysis algorithms in detail. This study indicates that (1) accuracy; (2) domain-specificity; (3) scalability; and (4) customizability, are four key components that significantly effect the algorithm’s adoption and enhance users’ satisfaction. Furthermore, the SWARA is used to determine the weights of attributes. The MABAC approach is then employed to address the decision-making problem scenario. We use a numerical case to compare the extended MABAC approach to other approaches to assess its validity in the field of computer science.

Multi-attribute group decision-making based on bipolar n,m-rung orthopair fuzzy sets

Multi-attribute group decision-making based on bipolar n,m-rung orthopair fuzzy sets

MAGDM Model Using an Intuitionistic Fuzzy Matrix Energy Method and Its Application in the Selection Issue of Hospital Locations

Matrix energy is a valid mathematical tool for representing collective information. However, it is not used in the existing literature for fuzzy sets, intuitionistic fuzzy sets, and multi-attribute group decision making (MAGDM) problems, which highlights research gaps. Motivated by both matrix energy and the research gaps, this study aims to extend matrix energy to the energy of an intuitionistic fuzzy matrix (IFM) and to utilize the IFM energy method in the MAGDM problem, which fully contains all the IFM information on attribute weights, decision maker weights, and attribute values. To achieve these objectives, this paper first proposes IFM energy in terms of the true matrix energy and false matrix energy; then, it develops a MAGDM model using the IFM energy method and the score and accuracy equations of IFM energy. Then, the developed MAGDM model is applied to the selection problem of hospital locations in Shaoxing City, China to demonstrate the practicality and validity of the developed model. Compared with existing intuitionistic fuzzy MAGDM methods, the developed MAGDM model using the IFM energy method reveals its superiority and novelty in complete IFM information expressions and the MAGDM method because the existing MAGDM methods containing intuitionistic fuzzy set information have difficulty tackling MAGDM problems containing all the IFM information on attribute weights, decision maker weights, and attribute values.

Multi-attribute group decision-making in site selection of solar photovoltaic cells under triangular Pythagorean fuzzy environment

Multi-attribute group decision-making in site selection of solar photovoltaic cells under triangular Pythagorean fuzzy environment

A novel decision-making approach based on interval-valued T-spherical fuzzy information with applications

Multi-attribute group decision-making (MAGDM) is very significant technique for selecting an alternative from the provided list. But the major problem is to deal with the information fusion during the information. Aczel-Alsina t-norm (AATN) and Aczel-Alsina t-conorm (AATCN) are the most generalized and flexible t-norm (TN) and t-conorm (TCN) which is used for information processing. Moreover, the interval-valued T-spherical fuzzy set (IVTSFS) is the latest framework to cover the maximum information from the real-life scenarios. Hence, the major contribution of this paper is to deal the information while the MAGDM process by introducing new aggregation operators (AOs). Consequently, the interval-valued T-spherical fuzzy (IVTSF), Aczel-Alsina weighted averaging (IVTSFAAWA), IVTSF Aczel-Alsina (IVTSFAA) ordered weighted averaging (IVTSFAAOWA), IVTSFAA weighted geometric (IVTSFAAWG), IVTSFAA ordered weighted geometric (IVTSFAAOWG), and IVTSFAA hybrid weighted geometric (IVTSFAAHWG) operators are developed. It is shown that the proposed operators are the valid and the results obtained are reliable by discussing some basic properties. To justify the developed AOs, an example of the MAGDM is discussed. The sensitivity of these AOs is observed keeping in view of the variable parameter. To show the importance of the newly developed theory, a comparison of the proposed AOs is established with already existing operators.

Group decision-making algorithm with sine trigonometric [formula omitted]-quasirung orthopair aggregation operators and their applications

p,q-quasirung orthopair fuzzy set (p,q-QOFS) is an extension of the q-rung orthopair fuzzy set (q-ROFS) theory used to represent uncertainty and vagueness in decision-making processes. The paper aims to utilize robust sine-trigonometric operational laws to investigate the group decision-making process within the p,q-QOFSs framework. The p,q-QOFS possess a distinctive characteristic of handling uncertain information by utilizing a wider space for membership representation compared to q-ROFS. Consequently, the current paper has been categorized into three distinct phases. In the initial phase, novel operational laws will be presented for p,q-QOFSs. The fundamental concept behind these newly proposed operations is to integrate the properties of the sine function, which include being periodic and symmetric about the origin, into the decision-making process for objects. Then, based on these laws, several operators for aggregating information will be obtained, along with their necessary properties and relationships. Lastly, an algorithm will be presented for interpreting the problem of multi attribute group decision-making (MAGDM), utilizing the operators, and demonstrating it with an illustrative example. A comprehensive comparative analysis will be conducted with some of the existing methods to uncover their impacts.

Multi-Attribute Group Decision Making Based on Spherical Fuzzy Zagreb Energy

Based on picture fuzzy sets (PFSs), we use a mathematical model to tackle such types of problems when a person has opinions like yes, no, abstain, and refusal. The spherical fuzzy model is more flexible and practical than the picture fuzzy model, as it enhances the space of uncertainty. It broadens the space of vague information evaluated by decision makers since graphs are the pictorial representation of information. Graphs are a tool to represent a network. To handle some real-world problems, spherical fuzzy graphs can be used more effectively as compared to picture fuzzy graphs (PFGs). In this article, we expand the notion of fuzzy Zagreb indices of the fuzzy graph to the spherical fuzzy Zagreb indices of the spherical fuzzy graph (SFG). The spherical fuzzy Zagreb matrix of SFG and Zagreb energy of SFG are defined with examples. Additionally, we develop several lower and upper bounds of the spherical Zagreb energy of SFG. In addition, we present an application of SFG by computing its Zagreb energy in the decision-making problem of choosing the best location for business purposes.

Multi-attribute group decision-making based on q-rung orthopair fuzzy Aczel–Alsina power aggregation operators

This article aims to develop the Aczel–Alsina operational laws based on power aggregation operators (PAOs) for the q-Rung orthopair fuzzy set (FS) (q-ROFS) framework. Changing the parameter can alter these sets according to the degree of fluctuation, providing a range of options. The decision-making sciences use “q-ROFS” to describe the family of the most distinct fuzzy information thoughts. In response to the degree of variation, the q-ROFSs can increasingly adjust the information region by fluctuating the restriction q≥1, making numerous options. But on the other hand, PAOs have the advantage of vanishing the influence of awkward data from the final results. To take advantages of POAs, based on Aczel- Alsina (AA) operational laws, q-Rung orthopair fuzzy (q-ROF) AA power-weighted averaging (q-ROFAAPWA) and q-ROF AA power-weighted geometric (q-ROFAAPWG) operators are stated. Also, it studied that the proposed AOs fulfilled the conditions of boundedness, monotonicity, and idempotency. Based on these newly constructed AOs, a technique for dealing the multi-attribute group decision-making (MAGDM) problems is suggested. Numerous research, correlations with another modern approach and a numerical example of selecting stock market companies have been used to show the suggested system’s applicability. The sensitivity analysis of the developed method is examined. A comparative study with other prevailing methods is also providing for superiority analysis. Finally, we demonstrated that in the results and discussion section, the proposed AOs in the q-ROPFS framework are more reliable in aggregating information than intuitionistic fuzzy (IF) set (IFS) and Pythagorean FS (PyFS) frameworks.

Some q-rung orthopair linguistic Heronian mean operators with their application to multi-attribute group decision making

Some q-rung orthopair linguistic Heronian mean operators with their application to multi-attribute group decision making

MABAC framework for logarithmic bipolar fuzzy multiple attribute group decision-making for supplier selection

In this article, we introduce logarithmic operations on bipolar fuzzy numbers (BFNs). We present some new operators based on these operations, namely, the logarithm bipolar fuzzy weighted averaging (L-BFWA) operator, logarithm bipolar fuzzy ordered weighted averaging (L-BFOWA) operator, and logarithm bipolar fuzzy weighted geometric (L-BFWG) operator, and logarithm bipolar fuzzy ordered weighted geometric (L-BFOWG) operator. Further, develop a multi-attribute group decision-making (MAGDM) methodology model based on logarithm bipolar fuzzy weighted averaging operator and logarithm bipolar fuzzy weighted geometric operators. To justify the proposed model’s efficiency, MABAC (the multiple attribute border approximation area comparison) methods are applied to construct MAGDM with BFNs established on proposed operators. To demonstrate the proposed approach’s materiality and efficiency, use the proposed method to solve supply chain management by considering numerical examples for supplier selection. The selection of suppliers is investigated by aggregation operators to verify the MABAC technique. The presented method is likened to some existing accumulation operators to study the feasibility and applicability of the proposed model. We concluded that the proposed model is accurate, effective, and reliable.

Probabilistic dual hesitant fuzzy MAGDM method based on generalized extended power average operator and its application to online teaching platform supplier selection

Probabilistic dual hesitation fuzzy set (PDHFS) is a new tool used to enhance fuzzy and imprecise information in a changing and complex decision-making environment. Compared with the existing fuzzy sets, PDHFS is more suitable for identifying uncertain evaluation data in complex and realistic decision-making situations. The extended power average (EPA) and generalized power average (GPA) operators provide more information for the information aggregation process, reflecting the mutual support among all aggregation values. To solve the evaluation problems related to decision analysis involving extremely complex information, especially in the case of extreme data in the consideration of decision problems. In this paper, the generalized extended power average (GEPA) operator is proposed in the PDHF environment. This research has the following four important contributions. First, we propose a probability splitting algorithm of normalizing PDHFE, and give some new basic operations, score function, distance measures and aggregation operators. Second, to better integrate the advantages of EPA and GPA operators, we developed the GEPA operator, weighted form (WGEPA) and ordered form (GEPOWA). Meanwhile, by adjusting the parameter in three types of operators, we can find that many existing operators are special forms of the three types of operators. Third, extended the three types of operators to the PDHF environment, the PDHFGEPA, PDHFWGEPA, PDHFGEPOWA operators and some special aggregation operators are obtained, and studied their valuable properties in detail. Finally, taking the application of PDHFWGEPA operator as an example, a new multi-attribute group decision-making (MAGDM) technique is developed and used to solve the problem of online teaching platform supplier selection (OTPSS). Through parameter sensitivity analysis, the flexibility and stability of MAGDM technique are illustrated, and compared with some existing decision-making methods, the effectiveness of MAGDM method is proved. In addition, this technique also provides some theoretical references for dealing with other complex MAGDM problems, and provides a new operator for the extension of GEPA operator in other fuzzy environments. The biggest advantage of several operators is that they can pay full attention to the special role of extreme data in decision-making.

MAGDM method based on generalized hesitant fuzzy TODIM and cumulative prospect theory and application to recruitment of university researchers

The recruitment of university researchers can be considered a multi-attribute group decision-making (MAGDM) problem. MAGDM is a familiar issue with uncertainty and fuzziness in the decision-making field. Generalized hesitation fuzzy numbers (GHFNs) as a new expanded form of hesitation fuzzy numbers (HFNs) can better express the uncertain information in MAGDM. The TODIM is a very classical and widely used method to deal with the MAGDM issue. In this paper, we integrate cumulative prospect theory (CPT) into TODIM to consider not only decision makers’ subjective risk preferences but also their confidence level to obtain more reasonable choices under risk conditions. Therefore, we propose the GHF CPT-TODIM approach to tackle the MAGDM issue. Meanwhile, in the GHF environment, it is proposed to use the volatility of attribute information (entropy weighting method) to obtain the importance of attributes, obtain the unknown attribute weight, and enhance the rationality of weight information. Finally, the validity and usefulness of the technique are verified by applying the GHF CPT-TODIM technique to the recruitment of university researchers and comparing it with the existing GHF MAGDM method, which offers a new way to solve the MAGDM problem with GHFNs.

A novel approach based on neutrosophic Bonferroni mean operator of trapezoidal and triangular neutrosophic interval environments in multi-attribute group decision making

Neutrosophic multicriteria is a method of decision-making that uses indeterminacy to combine several criteria or elements, frequently with incomplete or ambiguous information, to find a solution. The neutrosophic multicriteria analysis enables the assessment of qualitative and subjective aspects and can assist in resolving conflicting goals and preferences. In the Neutrosophic Multi-Attribute Group Decision Making (NMAGDM) problems, all the information provided by the decision makers (DMs) is expressed as single value neutrosophic triangular and trapezoidal numbers examined in this study which can provide more flexibility and accuracy in capturing uncertainty and aggregating preferences. We offer a novel approach for determining the neutrosophic possibility degree of two and three trapezoidal and triangular neutrosophic sets and the concepts of neutrosophic possibility mean value. The trapezoidal and triangular neutrosophic Bonferroni mean (TITRNBM) operator and the trapezoidal and triangular neutrosophic weighted Bonferroni mean (TITRNWBM) operator are two aggregation methods we then create. Further, we examine the TITRNBM and TITRNWBM attributes and their uniqueness. The NMAGDM approach with trapezoidal and triangular information is suggested based on the TITRNWBM operator and possibility degree. Finally, a concrete example of manufacturing companies searching for the best supplier for assembling the critical parts is provided to validate the established strategies and show their practical applicability and efficacy.

A Confidence-based Consensus Model for Multi-Attribute Group Decision Making: Exploring the Bounded Trust Propagation and Personalized Adjustment Willingness

A Confidence-based Consensus Model for Multi-Attribute Group Decision Making: Exploring the Bounded Trust Propagation and Personalized Adjustment Willingness