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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 method has been used to solve multiple attribute decision making (MADM) problems. The interval-valued Pythagorean fuzzy sets (IVPFSs) are useful tools to depict uncertainty of the MADM. In this paper, we will extend the TODIM method to the MADM with the interval-valued Pythagorean fuzzy numbers (IVPFNs). Firstly, the definition, comparison and distance of IVPFNs are briefly introduced, and the steps of the classical TODIM method for MADM problems are presented. Then, the extended classical TODIM method is proposed to deal with MADM problems with the IVPFNs, and its significant characteristic is that it can fully consider the decision makers' bounded rationality which is a real action in decision making. Furthermore, we develop the concept of the q-rung interval-valued orthopair fuzzy sets (q-RIVOFSs) and extend the TODIM method to q-RIVOFNs. Finally, a numerical example is proposed and a comparative analysis is given. [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 problems with interval-valued Pythagorean uncertain linguistic information. Then, we utilize arithmetic and geometric operations to develop some interval-valued Pythagorean uncertain linguistic aggregation operators: interval-valued Pythagorean uncertain linguistic weighted average (IVPULWA) operator, interval-valued Pythagorean uncertain linguistic weighted geometric (IVPULWG) operator, interval-valued Pythagorean uncertain linguistic ordered weighted average (IVPULOWA) operator, interval-valued Pythagorean uncertain linguistic ordered weighted geometric (IVPULOWG) operator, interval-valued Pythagorean uncertain linguistic hybrid average (IVPULHA) operator and interval-valued Pythagorean uncertain linguistic hybrid geometric (IVPULHG) operator, some interval-valued Pythagorean uncertain linguistic correlate aggregation operators, some interval-valued Pythagorean uncertain linguistic induced aggregation operators, some interval-valued Pythagorean uncertain linguistic induced correlate aggregation operators and some interval-valued Pythagorean uncertain linguistic power aggregating operators. The prominent characteristic of these proposed operators are studied. Then, we have utilized these operators to develop some approaches to solve the interval-valued Pythagorean uncertain linguistic multiple attribute decision making problems. Finally, a practical example for enterprise resource planning (ERP) system selection is given to verify the developed approach and to demonstrate its practicality and effectiveness. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the uncertain multiple attribute group decision making (MAGDM) problems in which the attributes and experts are in different priority level. Motivated by the idea of prioritized aggregation operators (Yager 2008), we develop some prioritized aggregation operators for aggregating uncertain information, and then apply them to develop some models for uncertain multiple attribute group decision making (MAGDM) problems in which the attributes and experts are in different priority level. Finally, a practical example about talent introduction is given to verify the developed approach and to demonstrate its practicality and effectiveness. [ABSTRACT FROM PUBLISHER]

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]

In this paper, we propose an uncertain linguistic hybrid geometric mean (ULHGM) operator, which is based on the uncertain linguistic weighted geometric mean (ULWGM) operator and the uncertain linguistic ordered weighted geometric (ULOWG) operator proposed by Xu [Z. S. Xu, "An approach based on the uncertain LOWG and induced uncertain LOWG operators to group decision making with uncertain multiplicative linguistic preference relations", Decision Support Systems41 (2006) 488–499] and study some desirable properties of the ULHGM operator. We have proved both ULWGM and ULOWG operators are the special case of the ULHGM operator. The ULHGM operator generalizes both the ULWGM and ULOWG operators, and reflects the importance degrees of both the given arguments and their ordered positions. Based on the ULWGM and ULHGM operators, we propose a practical method for multiple attribute group decision making with uncertain linguistic preference relations. Finally, an illustrative example demonstrates the practicality and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Point-, line- or boundary-sampled intercepts may be measured inside a particle as a measure of particle size. Each intercept is primarily characterized by three geometric properties: length, location and orientation. Circle and sphere models are used in the present study to analyze these properties. The probability distribution function, probability density function, expectation and coefficient of variation for each of the properties were presented based on geometric probability and mathematical statistics. Such presentation would be helpful for potential users of stereology to better understand the concept of intercepts and implement stereological intercept measurement for estimation of particle sizes in practice. [ABSTRACT FROM AUTHOR]