A New Approach to Neutrosophic Soft Sets and their Application in Decision Making

Abstract

In literature, several models which can handle uncertainty in datasets have been 
introduced. Fuzzy set introduced by Zadeh in 1965, is one of the earliest such models and 
Atanassov generalised it by introducing the notion of Intuitionistic fuzzy sets(IFS) in 1986. 
However, these models are handicaped due to their inadequacy as parameterization tools. The 
notion of Soft sets (SS) was introduced by Molodtsov in 1999 to solve this problem. Almost at the 
same time, Neutrosophic set (NS) model was introduced by Smarandache, which is a huge 
generalisation of IFS. As has been the practice, the hybrid model of SS and NS was proposed to 
frame the notion of Neutrosophic Soft Set (NSS) by Ali and Smaranche in 2015 and studied their 
properties. Since its inception, one of the major areas of application of Soft Sets has been that of 
Multi-criterian Decision Making (MCDM). Many problems in MCDM were solved by using hybrid 
models of SS. Following this trend, in this paper, we develop an algorithm basing upon NSS to 
handle the problem of MCDM in the selection of faculty through an interview prcess. For this, we 
had to introduce an improved score function which is used to rank the candidates basing upon 
several of their characteristics including interview perfromances. This application is better handled 
by the NSS model as is evident from the results. We illustrated the superiority of our proposed 
algorithm by providing a comparative analysis with many exieting algorithms in the literature.