N-intuitionistic polygonal fuzzy aggregation operators and their application to multi-Attribute decision making

Abstract

The n-intuitionistic polygonal fuzzy set (n-IPFS), combined by the intuitionistic fuzzy and polygonal fuzzy sets, is an extended form of the triangular intuitionistic fuzzy set (TIFS) and the trapezoidal intuitionistic fuzzy set (TrIFS). The aim of this paper is to develop some new aggregation operators for n-IPFSs and apply them to multi-Attribute group decision making (MAGDM) problems. First, the operational properties and the score function of n-IPFSs are defined. Then, three kinds of n-intuitionistic polygonal fuzzy aggregation operators are investigated including n-intuitionistic polygonal fuzzy weighted averaging (n-IPFWA) operator, n-intuitionistic polygonal fuzzy ordered weighted averaging (n-IPFOWA) operator and n-intuitionistic polygonal fuzzy hybrid aggregation (n-IPFHA) operator. Finally, we propose an improved technique for order preference by similarity to an ideal solution (TOPSIS) approach with n-IPFSs and unknown attributes weights. The attributes weights are obtained by combining the entropy weights and the subjective weights, and the entropy weights are calculated based on the score function of n-IPFS. The spatial closeness refiected by the Hamming distance and the grey relationship with the positive/negative solution are both considered in getting the relative closeness degree to rank the alternatives. The example analysis of a location selection is given to verify the practicality and the effectiveness of the proposed approach in this paper.