An Improved Aggregation Operators-based Method for Multiple Attribute Group Decision Making Using Interval-valued Trapezoidal Intuitionistic Fuzzy Sets
Affiliations: [a] Decision Sciences Institute, Fuzhou University, Fuzhou, Fujian, P.R. China | [b] Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou University, Fuzhou, Fujian P.R. China
Correspondence:
[*]
Corresponding author. Ying-Ming Wang, School of Economics and Management, Fuzhou University, Fuzhou, Fujian 350116, P.R. China. Tel.:+86 59122866677; E-mail: [email protected].
Abstract: This paper investigates a method to deal with multiple attribute group decision making (MAGDM) problems in which the decision makers’ weights are expressed as crisp numbers, the weights of attributes are unknown, and attribute values are expressed as interval-valued trapezoidal intuitionistic fuzzy numbers (IVTrIFNs). Firstly, some new aggregation operators are proposed, including the interval-valued trapezoidal intuitionistic fuzzy weighted geometric averaging (IVTrIFWGA) operator, the interval-valued trapezoidal intuitionistic fuzzy ordered weighted geometric averaging(IVTrIFOWGA) operator, and the interval-valued trapezoidal intuitionistic fuzzy hybrid geometric averaging (IVTrIFHGA) operator. Some desirable properties of these operators are studied. The results of using these operators for aggregation are also interval-valued trapezoidal intuitionistic fuzzy numbers. Secondly, a fuzzy cross-entropy of interval-valued trapezoidal intuitionistic fuzzy sets(IVTrIFSs) is defined, based on which a new mathematical model is established to determine the weights of attributes. Finally, the fuzzy grey relation analysis (GRA) is utilized to rank decision alternatives. Numerical examples are provided to demonstrate the effectiveness of the proposed multiple attribute group decision making method and its advantages in overcoming the defects of the existing methods.
