An integrating OWA–TOPSIS framework in intuitionistic fuzzy settings for multiple attribute decision making

Abstract

In this paper, we develop an integrating OWA–TOPSIS approach in intuitionistic fuzzy environment to tackle fuzzy multiple attribute decision making problems. The proposed intuitionistic fuzzy OWA–TOPSIS method provides a general framework of diverse fuzzy information aggregation process including different determination methods of extreme points. There are six different types of information aggregation (s–p–d type, p–s–d type, s–d–p type, p–d–s type, d–s–p type and d–p–s type) following the different sequences of source aggregation, preference aggregation. During the different aggregation scenarios, positive ideal points and negative ideal points are identified as a point, a vector or a matrix. A real application example is provided to demonstrate in detail the proposed approach. The comparative results in total 32 experiments show the rankings consistency and different levels of information loss in the six different aggregation types. On the whole, the ranks are most precise in d–s–p and d–p–s types, and more precise in s–p–d and p–s–d types than that in s–d–p and p–d–s types.