A New Approach in Multiple Attribute Decision Making using R-norm entropy and Hamming Distance Measure

10.6186/IJIMS.2016.27.3.3

Rajesh Joshi；Satish Kumar

Intuitionistic fuzzy entropy ； R-norm intuitionistic fuzzy entropy ； MADM ； TOPSIS ； weighted Hamming distance

International Journal of Information and Management Sciences

27卷3期（2016 / 09 / 01）

253 - 268

英文

The theory of intuitionistic fuzzy (IF) set is well suitable to deal with the vagueness and hesitancy. In the present communication, we have considered R-norm entropy with both uncertainty and hesitancy degree of an IF set. Using this R-norm entropy, we have solved a multiple attribute decision making (MADM) problem in which attribute values are expressed with IF values. In MADM problem, we mainly encounter with two types of problems. First is when we don’t have any information regarding attribute weights and second is when we have little information about weights, i.e., they are partially known to us. In this paper, we have considered both the cases with examples. For the first case, we have used an extension of entropy weight method to calculate the attribute weights and in second case attribute weights are calculated by using the minimum entropy principle method which is based on solving a linear programming model. The two methods are effectively explained by taking real life examples. Also the two examples are calculated by using the TOPSIS method suggested by Chen and Tsao and shown that the outputs of both the methods coincide.

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