Methods for Multiple Attribute Group Decision Making Based on Intuitionistic Fuzzy Dombi Hamy Mean Operators

Abstract

In this paper, we extended the Hamy mean (HM) operator, the Dombi Hamy mean (DHM) operator, the Dombi dual Hamy mean (DDHM), with the intuitionistic fuzzy numbers (IFNs) to propose the intuitionistic fuzzy Dombi Hamy mean (IFDHM) operator, intuitionistic fuzzy weighted Dombi Hamy mean (IFWDHM) operator, intuitionistic fuzzy Dombi dual Hamy mean (IFDDHM) operator, and intuitionistic fuzzy weighted Dombi dual Hamy mean (IFWDDHM) operator. Following this, the multiple attribute group decision-making (MAGDM) methods are proposed with these operators. To conclude, we utilized an applicable example for the selection of a car supplier to prove the proposed methods.

The generalized Dice similarity measures for Pythagorean fuzzy multiple attribute group decision making

“…Multiple attribute decision making is the main branch of decision theory, the theory of intuitionistic fuzzy sets (IFSs) proposed by Atanassov has been widely used in dealing with problems with imprecision and uncertainty. Li et al proposed the intuitionistic fuzzy Dombi Hamy mean (IFDHM) operator, intuitionistic fuzzy weighted Dombi Hamy mean operator, intuitionistic fuzzy Dombi dual Hamy mean operator, and intuitionistic fuzzy weighted Dombi dual Hamy mean operator. In this theory, the relationship between an element and a set is described by two numbers, which represent, respectively, the membership degree and nonmembership degree of the element to the set.…”

“…Therefore, it is necessary to extend the Dice measure to PFSs to handle patterns recognition, citation analysis, information retrieval, and multiple attribute group decisionmaking problems to satisfy the requirements of decision makers' preference and flexible decision making. To do so, the main purposes of this study are: (1) to propose two forms of the Dice measures of PFSs, (2) to present the generalized Dice measures of PFSs, and (3) to develop the generalized Dice measures-based multiple attribute group decision-making models of PFSs. In the multiple attribute group decision-making process, the main advantage of the proposed methods is more general and more flexible than existing patterns recognition methods with PFSs to satisfy the practical requirements.…”

Grey Fuzzy Multiple Attribute Group Decision-Making Methods Based on Interval Grey Triangular Fuzzy Numbers Partitioned Bonferroni Mean

“…Multiple attribute group decision-making (MAGDM) is the process of comparing and choosing the best alternative by analyzing the evaluations of multiple attributes aggregated from decision-makers. And MAGDM problems have a wide application in real life, such as effect evaluation of environmental protection policies formulated by the government [1], the evaluation of international cooperation plans for energy development [2], the assessment of the comprehensive strength of different schools [3], the comparison of various schemes by enterprises in business negotiations [4], and the selection of enterprises' purchasing schemes [5]. Most of the MAGDM problems exist fuzziness and uncertainty, which is because people's ideology is subjective and complex.…”

Some q ‐rung orthopair fuzzy Hamy mean operators in multiple attribute decision‐making and their application to enterprise resource planning systems selection

“…More recently, Pythagorean fuzzy set (PFS) has appeared as an effective and useful tool for depicting uncertainty of the multiple attribute decision‐making (MADM) problems . The PFS is also characterized by the membership degree and the nonmembership degree, whose sum of squares is less than or equal to 1, and the PFS is more general than intuitionistic fuzzy set (IFS) . In some cases, the PFS can solve some problems that the IFS cannot, for example, if a DM gives the membership degree and the nonmembership degree as 0.8 and 0.6, respectively; then it is only valid for the PFS.…”

“…[2][3][4][5][6][7][8] The PFS is also characterized by the membership degree and the nonmembership degree, whose sum of squares is less than or equal to 1, and the PFS is more general than intuitionistic fuzzy set (IFS). [9][10][11][12][13][14][15][16][17] In some cases, the PFS can solve some problems that the IFS cannot, for example, if a DM gives the membership degree and the nonmembership degree as 0.8 and 0.6, respectively; then it is only valid for the PFS. In other words, all the intuitionistic fuzzy degrees are a part of the Pythagorean fuzzy degrees, which indicates that the PFS is more powerful and useful to handle the uncertain decision-making problems.…”