Group decision making using neutrosophic soft matrix: An algorithmic approach - Sujit Das; Saurabh Kumar; Samarjit Kar; Tandra Pal (Journal of King Saud University: Computer and Information Sciences, 2019)
This article proposes an algorithmic approach for group decision making (GDM) problems using neutrosophic soft matrix (NSM) and relative weights of experts. NSM is the matrix representation of neutrosophic soft sets (NSSs), where NSS is the combination of neutrosophic set and soft set. We propose a new idea for assigning relative weights to the experts based on cardinalities of NSSs. The relative weight is assigned to each of the experts based on their preferred attributes and opinions, which reduces the chance of unfairness in the decision making process. Firstly we introduce choice matrix and combined choice matrix using neutrosophic sets. Multiplying combined choice matrices with the individual NSMs, this study develops product NSMs, which are aggregated to find out the collective NSM. Then neutrosophic cross-entropy measure is used to rank the alternatives and for selecting the most desirable one(s). This study also provides a comparative analysis of the proposed weight based approach with the normal procedure, where weight is not considered. Finally, a case study illustrates the applicability of the proposed approach. Keywords: Group decision making, Neutrosophic soft matrix, Choice matrix, Relative weight