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In multiple attribute decision making (MADM) issues, the ambiguity, imprecision, and imperfection of assessment information may lead to inadequate decision-making results. However, the Z-number suggested by Zadeh in 2011 could somehow prevent this problem. For MADM issues with unknown attributes weights, an extended Distance from the Average Solution (EDAS) method is proposed under a mixture Z-number environment. In addition, the Criteria Importance Through Inter-criteria Correlation (CRITIC) method is used to estimate the weights of the criterion, which is easy to calculate and avoids subjective forecasts. A novel illustrative example is provided to demonstrate the feasibility, validity, and practicability of the presented method, and is compared with existing decision methods. The outcome indicates that the suggested method can solve complicated decision-making problems. [ABSTRACT FROM AUTHOR]

Recently, the TODIM (an acronym in Portuguese for Interactive Multi-criteria Decision Making) approach, which can characterize the decision makers’ psychological behaviors under risk, has been introduced to handle multiple attribute decision making (MADM) problems. The Pythagorean 2-tuple linguistic sets (P2TLSs) are useful tools to depict uncertainty in MADM. This paper use TODIM method to solve MADM with the Pythagorean 2-tuple linguistic numbers (P2TLNs). Firstly, the definition, comparison and distance of P2TLNs are briefly presented, and classical TODIM for MADM are introduced. Then, the extended classical TODIM method is proposed to deal with MADM problems with P2TLNs, and its important characteristic is to fully consider the decision makers’ bounded rationality which is a real action in MADM. Furthermore, we extend TODIM method to Pythagorean interval 2-tuple linguistic sets (PI2TLSs). Finally, a numerical example is proposed and a comparative analysis is also given. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the multiple attribute decision making (MADM) problems with 2-tuple linguistic information. Motivated by the ideal of Bonferroni mean and geometric Bonferroni mean, we develop two aggregation techniques called the 2-tuple linguistic Bonferroni mean (2TLBM) operator and the 2-tuple linguistic geometric Bonferroni mean (2TLGBM) operator for aggregating the 2-tuple linguistic information. We study its properties and discuss its special cases. For the situations where the input arguments have different importance, we then define the 2-tuple linguistic weighted Bonferroni mean (2TLWBM) operator and the 2-tuple linguistic weighted geometric Bonferroni mean (2TLWGBM) operator, based on which we develop two procedure for multiple attribute decision making under the 2-tuple linguistic environments. Finally, a practical example with comprehensive evaluating modeling of brand extension is given to verify the developed approach and to demonstrate its practicality and effectiveness. [ABSTRACT FROM AUTHOR]