A New Multi-attribute Decision Making Method Based on Interval Normal Type-2 Fuzzy Numbers

Author(s):  
Huidong Wang ◽  
Jinli Yao ◽  
Xiaoyun Zhang
2011 ◽  
Vol 58-60 ◽  
pp. 869-874
Author(s):  
Hong An Zhou

The fuzzy multi-attribute decision-making (FMADM) problems, in which the information about attribute weights is partly known, the attribute values take the form of triangular fuzzy numbers, and the decision maker (DM) has fuzzy reciprocal preference relation on alternatives, are investigated. Firstly, some concepts, such as the multiply between two triangular fuzzy numbers, the projection of triangular fuzzy numbers vectors, etc, are given. Secondly, in order to reflect to the DM’s subjective preference information on alternatives, we make the objective decision information uniform by using a translation function and establish a goal programming model, and then the attribute weights is obtained by solving the model, thus the weighted attribute values of all alternatives are gained. The concept of fuzzy positive ideal solution (FPIS) of alternatives is introduced, and the alternatives are ranked by using the projection of the weighted attribute values of every alternative on FPIS. The method not only can sufficiently utilize the objective information and meet the DM’s subjective preferences on alternatives as much as possible, but also it is characterized by simple operation and easy to implement on a computer. Finally, a practical example is illustrated to show the feasibility and availability of the developed method.


Filomat ◽  
2017 ◽  
Vol 31 (2) ◽  
pp. 431-450 ◽  
Author(s):  
Jing Wang ◽  
Qing-Hui Chen ◽  
Hong-Yu Zhang ◽  
Xiao-Hong Chen ◽  
Jian-Qiang Wang

Type-2 fuzzy sets (T2FSs) are the extension of type-1 fuzzy sets (T1FSs), which can convey more uncertainty information in solving multi-criteria decision-making (MCDM) problems. Motivated by the extension from interval numbers to triangular fuzzy numbers, three-trapezoidal-fuzzy-number-bounded type-2 fuzzy numbers (TT2FNs) are defined on the basis of interval type-2 trapezoidal fuzzy numbers (IT2TFNs), and they can convey more uncertainty information than T1FSs and IT2FSs. Moreover, the drawbacks of the existing computational models of generalized fuzzy numbers are analyzed, and a new computational model of fuzzy numbers is proposed, which is further extended to TT2FNs. Besides, a MCDM method is proposed to deal with the evaluation information given in the form of TT2FNs. Finally, an illustrative example and comparison analysis are provided to demonstrate the feasibility and validity of the proposed method.


Author(s):  
Beyza Ahlatcioglu Ozkok ◽  
Hale Gonce Kocken

Analytic hierarchy process (AHP) is a widely used multi-attribute decision-making (MADM) approach. Due to the complexity and uncertainty involved in real world problems, decision makers might be prefer to make fuzzy judgments instead of crisp ones. Furthermore, even when people use the same words, individual judgments of events are invariably subjective, and the interpretations that they attach to the same words may differ. This is why fuzzy numbers has been introduced to characterize linguistic variables. Fuzzy AHP methods have recently been extended by using type-2 fuzzy sets. Type-2 fuzzy set theory incorporates the uncertainty of membership functions into the fuzzy set theory. In this chapter, the authors firstly provide a short review on applications of interval type-2 fuzzy AHP on MADM problems. Then, they present a very efficient MADM technique, interval type-2 fuzzy AHP, to solve the portfolio selection problem that is to decide which stocks are to be chosen for investment and in what proportions they will be bought. And finally, they provided a case study on BIST.


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