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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]

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]

Abstract: The aim of this paper is to investigate the multiple attribute decision making problems with intuitionistic fuzzy information, in which the information about attribute weights is incompletely known, and the attribute values take the form of intuitionistic fuzzy numbers. In order to get the weight vector of the attribute, we establish an optimization model based on the basic ideal of traditional gray relational analysis (GRA) method, by which the attribute weights can be determined. For the special situations where the information about attribute weights is completely unknown, we establish another optimization model. By solving this model, we get a simple and exact formula, which can be used to determine the attribute weights. Then, based on the traditional GRA method, calculation steps for solving intuitionistic fuzzy multiple attribute decision-making problems with incompletely known weight information are given. Furthermore, we have extended the above results to an interval-valued intuitionistic fuzzy environment and developed modified GRA method for interval-valued intuitionistic fuzzy multiple attribute decision-making with incompletely known attribute weight information. Finally, an illustrative example is given to verify the developed approach and to demonstrate its practicality and effectiveness. [Copyright &y& Elsevier]

Abstract: To study the problem of multiple attribute decision making in which the decision making information values are triangular fuzzy number, a new group decision making method is proposed. Then the calculation steps to solve it are given. As the key step, a new operator called fuzzy induced ordered weighted harmonic mean (FIOWHM) operator is proposed and a method based on the fuzzy weighted harmonic mean (FWHM) operator and FIOWHM operators for fuzzy MAGDM is presented. The priority based on possibility degree for the fuzzy multiple attribute decision making problem is proposed. At last, a numerical example is provided to illustrate the proposed method. The result shows the approach is simple, effective and easy to calculate. [ABSTRACT FROM AUTHOR]

Abstract: With respect to multiple attribute group decision-making problems with linguistic information of attribute values and weight values, a group decision analysis is proposed. Some new aggregation operators are proposed: the extended 2-tuple weighted geometric (ET-WG) and the extended 2-tuple ordered weighted geometric (ET-OWG) operator and properties of the operators are analyzed. Then, A method based on the ET-WG and ET-OWG operators for multiple attribute group decision-making is presented. In the approach, alternative appraisal values are calculated by the aggregation of 2-tuple linguistic information. Thus, the ranking of alternative or selection of the most desirable alternative(s) is obtained by the comparison of 2-tuple linguistic information. Finally, a numerical example is used to illustrate the applicability and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

With respect to linguistic multiple attribute group decision making problems with incomplete weight information, a new method is proposed. In the method, the 2-tuple linguistic representation developed in recent years is used to aggregate the linguistic assessment information. In order to get the weight vector of the attribute, we establish an optimization model based on the basic ideal of traditional technique for order performance by similarity to ideal solution, by which the attribute weights can be determined. Then, the optimal alternative(s) is determined by calculating the shortest distance from the 2-tuple linguistic positive ideal solution, and on the other side, the farthest distance of the 2-tuple linguistic negative ideal solution. The method has exact characteristic in linguistic information processing. It avoided information distortion and losing, which occur formerly in the linguistic information processing. Finally, a numerical example is used to illustrate the use of the proposed method. The result shows the approach is simple, effective, and easy to calculate. [ABSTRACT FROM AUTHOR]