Group decision-making analysis with complex spherical fuzzy N-soft sets.
Journal:
Mathematical biosciences and engineering : MBE
Published Date:
Mar 16, 2022
Abstract
This paper develops the ELiminating Et Choice Translating REality (ELECTRE) method under the generalized environment of complex spherical fuzzy $ N $-soft sets ($ CSFN\mathcal{S}_{f}Ss $) that have distinctive and empirical edge of non-binary parametrization and also indeed overcome the limitations and flaws of existing ELECTRE I methods. We propose an innovatory decision-making technique, namely, $ CSFN\mathcal{S}_{f} $-ELECTRE I method where the data and information are in modern modes. The proposed $ CSFN\mathcal{S}_{f} $-ELECTRE I method enjoys all the distinct and modern attributes of uncertain information which mainly comprises of parameterizations, neutral perspective, multi-valuation and two-dimensional representations. We support the proposed work by a flowchart along with an algorithm and then utilize it to solve the MAGDM problem under $ CSFN\mathcal{S}_{f} $ environment. This novel technique employs the principles of $ CSFN\mathcal{S}_{f} $ concordance and $ CSFN\mathcal{S}_{f} $ discordance sets which are established on score and accuracy functions and engrossed to enjoin the most superior alternative. Ultimately, the decision graph and aggregated outranking Boolean matrix are formulated by merging the outcomes of $ CSFN\mathcal{S}_{f} $ concordance and $ CSFN\mathcal{S}_{f} $ discordance indices which are evaluated through score function and distance measures, respectively. Moreover, linear-ranking order is evaluated which provides linear ordering of decision alternatives. A prime MAGDM problem of poverty alleviation is addressed from socio-economic field that approve the flexibility of the intended approach. We perform a sustaining comparison with another approach (CSF-ELECTRE I approach) to assure the productivity and potency of the proposed methodology. We also provide an allegorical line graph of this comparison that demonstrate the admissibility of the resulting outcomes.