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Abstract
A new inclusion called total inclusion relation has improved the existing dissimilarity measure for q-rung orthopair fuzzy sets (qROFSs). For qROFSs, the modified axiomatic definition of dissimilarity measure is proposed. The modified Hamming and Euclidean dissimilarity measures are defined. An algorithmic procedure for a robust VIKOR method based on modified dissimilarity measures is established. The application of the robust VIKOR method in Mass Vaccination Campaigns (MVCs) in the COVID-19 situation is given.
Highlights
To measure the similarity between any form of data is an important topic
To improve the VIKOR method that produce the compromise solution which is closest to the positive ideal (PI) solution and it is applied to select the region to implement Mass Vaccination Campaigns (MVCs) in COVID-19 situations
We present the process of multi-criteria decision-making (MCDM) and VIKOR method based on the modified dissimilarity measures
Summary
To measure the similarity between any form of data is an important topic. The measures used to find the resemblance between data is called similarity measure. When a decision-maker provides such types of information but the sum of membership and non-membership degrees exceeds 1, the IFS is not able to cope with it For this reason, the theory of Pythagorean fuzzy set (PyFS) was proposed by Yager [6], [7] by modifying the ideology of IFS. PyFS has been implemented in numerous areas, but there were issues, when a decision-maker provides such types of information whose sum of the squares is exceeded from the unit interval, the PyFS is not able to cope with it For this reason, the theory of q-rung orthopair fuzzy set (qROFS) was proposed by Yager by modifying the ideology of PyFS [8], [9]. The method to choose particular region for implementation of MVCs is discussed in this article
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