In this paper, firstly, we propose two new GTHFNs-prioritized aggregation operators called generalized trapezoidal hesitant fuzzy number prioritized weighted average operator and generalized trapezoidal hesitant fuzzy number prioritized weighted geometric operator. Secondly, we investigate the fundamental properties of the operators in detail such as idempotency, boundedness and monotonicity. Thirdly, we propose a method based on the developed GTHF-numbers prioritized aggregation operators for solving an MADM problem with GTHF-numbers. Fourthly, we give a numerical example of the developed method. Finally, a comparative analysis is given with some existing methods in solving an MADM problem with GTHF-numbers.
Pub. online:1 Jan 2017Type:Research ArticleOpen Access
Journal:Informatica
Volume 28, Issue 2 (2017), pp. 387–402
Abstract
This paper proposes the concepts of a neutrosophic number and a trapezoidal neutrosophic number (TNN), the basic operational relations of TNNs, and the score function of TNN. Then, we develop a trapezoidal neutrosophic weighted arithmetic averaging (TNWAA) operator and a trapezoidal neutrosophic weighted geometric averaging (TNWGA) operator to aggregate TNN information and investigate their properties. Furthermore, a multiple attribute decision making method based on the TNWAA and TNWGA operators and the score function of TNN is established under a TNN environment. Finally, an illustrative example of investment alternatives is given to demonstrate the application and effectiveness of the developed approach.
Journal:Informatica
Volume 27, Issue 1 (2016), pp. 179–202
Abstract
This paper proposes the concept of an interval neutrosophic hesitant fuzzy set (INHFS) and the operational relations of INHFSs. Then, we develop correlation coefficients of INHFSs and investigate the relation between the similarity measures and the correlation coefficients. Furthermore, a multiple attribute decision making method based on the correlation coefficients is established under interval neutrosophic hesitant fuzzy environment. Through the correlation coefficients between each alternative and the ideal alternative, we obtain the ranking order of all alternatives and the best one. Finally, an illustrative example of investment alternatives is given to demonstrate the application and effectiveness of the developed approach.