Multi-attribute decision-making based on aggregations and similarity measures of neutrosophic hypersoft sets with possibility setting

ABSTRACT

Possibility neutrosophic hypersoft set is the generalisation of possibility neutrosophic soft set, and it is the blend of possibility neutrosophic set and hypersoft set. It tackles the insufficiencies of possibility intuitionistic fuzzy set and soft set for the entitlement of degree of indeterminacy and multi-argument approximate function, respectively. This kind of function maps the sub-parametric tuples to the power set of universe. It emphasises the partitioning of each attribute into its attribute-valued set that is missing in the existing fuzzy soft set-like structures. These features make it a completely new mathematical tool for solving problems dealing with uncertainties. This makes the decision-making process more flexible and reliable. This research aims to initiate a novel structure that hybridises the possibility neutrosophic set with the hypersoft set for dealing with uncertainties efficiently. Therefore, the possibility neutrosophic hypersoft set is developed by employing theoretic, axiomatic, graphical and algorithmic approaches. After conceptual characterisation of its essential elementary notions, decision-support systems are presented based on AND and OR operations with the proposal of algorithms to assist the decision-making process. Similarity measures between possibility neutrosophic hypersoft sets are elaborated with the support of daily life applications. The comparison of the proposed work is discussed with the existing relevant models under certain evaluating features.

KEYWORDS:

• Neutrosophic soft set
• neutrosophic hypersoft set
• possibility neutrosophic soft set
• decision-support system
• similarity measures

Acknowledgments

The authors are thankful to referees for their valuable suggestions to enhance the quality of the work.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was not supported by any specific funding.