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

Pythagorean fuzzy sets, originally proposed by Yager (IFSA world congress and NAFIPS annual meeting (IFSA/NAFIPS), pp 57-61, 2013), which are convenient to describe vagueness and impression in real-world problems, can be regarded as a novel extension of intuitionistic fuzzy sets. Generalized Maclaurin symmetric mean comes as an extension of classical Maclaurin symmetric mean. Their prominent feature is that they can both capture overall interrelationships and also focus on individual importance among the arguments. However, the generalized Maclaurin symmetric mean can only be considered for aggregating numeric information. In this paper, we investigate the generalized Maclaurin symmetric mean and further extend to Pythagorean fuzzy environment. We develop the Pythagorean fuzzy generalized Maclaurin symmetric mean (PFGMSM), the Pythagorean fuzzy weighted generalized Maclaurin symmetric mean (PFWGMSM), and discuss a variety of their desirable properties. To better understand the proposed parameterization operators, we provide an innovative way to extend the classical superiority and inferiority ranking (SIR) group decision model to Pythagorean fuzzy environment. Then, an approach formed with the aid of PFWGMSM and SIR to multiple attribute group decision-making with Pythagorean fuzzy information is developed. Finally, an illustrative example concerning supply chain financial risk decision-making is provided to verify the proposed method and demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the multiple-attribute decision-making (MADM) problems based on traditional Maclaurin symmetric mean operator operating under hesitant fuzzy environment. The Maclaurin symmetric mean (MSM) operator is a classical mean type aggregation operator and has strong modeling capability in modern information fusion theory, which has particular advantages for aggregating multi-dimension arguments. The prominent characteristic of the MSM operator is that it can capture the interrelationship among the multi-input arguments. Motivated by the idea of MSM operator, we develop the hesitant fuzzy Maclaurin symmetric mean (HFMSM) operator for aggregating the hesitant fuzzy information. Some desirable properties such as monotonicity, boundedness, idempotency are studied. Furthermore, we have discussed some special cases with respect to different parameter values of the HFMSM operator in detail. For the situations where the input arguments have different importance, we further develop the weighted hesitant fuzzy Maclaurin symmetric mean operator to aggregate hesitant fuzzy information. Based on which, an approach to MADM problems with hesitant fuzzy information is developed. Finally, a practical example with paper quality evaluation of sciencepaper online in China is provided to illustrate the practicality and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

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

Personnel selection is a critical enterprise strategic problem in knowledge-intensive enterprise. Fuzzy number which can be described as triangular (trapezoid) fuzzy number is an adequate way to assess the evaluation and weights for the alternatives. In that case, fuzzy TOPSIS, as a classic fuzzy multiple criteria decision making (MCDM) methods, has been applied in personnel selection problems. Currently, all the researches on this topic either apply crisp relative closeness but causing information loss, or employ fuzzy relative closeness estimate but with complicated computation to rank the alternatives. In this paper, based on Karnik–Mendel (KM) algorithm, we propose an analytical solution to fuzzy TOPSIS method. Some properties are discussed, and the computation procedure for the proposed analytical solution is given as well. Compared with the existing TOPSIS method for personnel selection problem, it obtains accurate fuzzy relative closeness instead of the crisp point or approximate fuzzy relative closeness estimate. It can both avoid information loss and keep computational efficiency in some extent. Moreover, the global picture of fuzzy relative closeness provides a way to further discuss the inner properties of fuzzy TOPSIS method. Detailed comparisons with approximate fuzzy relative closeness method are provided in personnel selection application. [ABSTRACT FROM AUTHOR]

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