淡江大學機構典藏:Item 987654321/109393
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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/109393


    Title: A New Approach in Multiple Attribute Decision Making using R-norm entropy and Hamming Distance Measure
    Authors: Rajesh Joshi;Satish Kumar
    Keywords: Intuitionistic fuzzy entropy;R-norm intuitionistic fuzzy entropy;MADM;TOPSIS;weighted Hamming distance
    Date: 2016
    Issue Date: 2017-02-13 15:57:36 (UTC+8)
    Publisher: 淡江大學出版中心
    Abstract: 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.
    Relation: International Journal of Information and Management Sciences 27(3), pp.253-268
    DOI: 10.6186/IJIMS.2016.27.3.3
    Appears in Collections:[International Journal of Information and Management Sciences] v.27 n.3

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