Title
An integrating OWA-TOPSIS framework in intuitionistic fuzzy settings for multiple attribute decision making.
Abstract
An integrating OWA-TOPSIS framework in intuitionistic fuzzy settings is proposed.The Friedman test verifies the rankings consistency of the six aggregation types.There exists different information loss in the six different aggregation processes.The ranks are most precise in d-s-p and d-p-s types. 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.
Year
DOI
Venue
2016
10.1016/j.cie.2016.05.029
Computers & Industrial Engineering
Keywords
Field
DocType
Intuitionistic fuzzy numbers,Fuzzy TOPSIS,Information loss,Multiple attribute group decision making
Extreme point,Friedman test,Aggregation problem,Data mining,Information loss,Fuzzy set operations,Fuzzy logic,Artificial intelligence,TOPSIS,Fuzzy number,Machine learning,Mathematics
Journal
Volume
Issue
ISSN
98
C
0360-8352
Citations 
PageRank 
References 
12
0.50
21
Authors
4
Name
Order
Citations
PageRank
Tianri Wang1120.84
Juan Liu2120.50
Jizu Li3120.50
Chonghuai Niu4120.50