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Study on the ranking problems in multiple attribute decision making based on interval‐valued intuitionistic fuzzy numbers
Author(s) -
Hao Yonghua,
Chen Xinguo
Publication year - 2018
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.21951
Subject(s) - ranking (information retrieval) , operator (biology) , mathematics , interval (graph theory) , set (abstract data type) , fuzzy number , group decision making , fuzzy set , discrete mathematics , algorithm , fuzzy logic , computer science , artificial intelligence , combinatorics , biochemistry , chemistry , repressor , transcription factor , political science , law , gene , programming language
This paper puts forward a new ranking method for multiple attribute decision‐making problems based on interval‐valued intuitionistic fuzzy set (IIFS) theory. First, the composed ordered weighted arithmetic averaging operator and composed ordered weighted geometric averaging operator are extended to the IIFSs in which they are, respectively, named interval‐valued intuitionistic fuzzy composed ordered weighted arithmetic averaging (IIFCOWA) operator and interval‐valued intuitionistic composed ordered weighted geometric averaging (IIFCOWG) operator. Afterwards, to compare interval‐valued intuitionistic fuzzy numbers, we define the concepts of the maximum, the minimum, and ranking function. Some properties associated with the concepts are investigated. Using the IIFCOWA or IIFCOWG operator, we establish the detailed steps of ranking alternatives (or attributes) in multiple attribute decision making. Finally, an illustrative example is provided to show that the proposed ranking method is feasible in multiple attribute decision making.