An integrated approach of complex fuzzy hypersoft set and possibility degree setting to manage uncertainties associated with multi-attribute decision-making
Abstract
Evaluating cars is a common multi-attribute decision-making (MADM) problem because it requires considering multiple criteria at once. However, assessing these attributes often involves subjective judgments and incomplete data. Consequently, the decision-making process contains inherent uncertainties that necessitate advanced models to handle imprecise and ambiguous evaluations. This paper is aimed at developing novel complex hybrid hypersoft set structures: the possibility complex fuzzy hypersoft set (pCFHS), the possibility complex intuitionistic fuzzy hypersoft set (pCIFHS), and the possibility complex neutrosophic hypersoft set (pCNHS), by simultaneously integrating complex fuzzy hypersoft sets, complex intuitionistic fuzzy hypersoft sets, and complex neutrosophic hypersoft sets with possibility degree-based arrangements. Using the aggregations of three proposed frameworks, three algorithms are presented and validated through the MADM problem of car evaluation. This evaluation considers four main attributes along with eight corresponding sub-attribute values. Importantly, the study incorporates fuzzy values to reflect degrees of possibility. This possibility grade is meant to assess the biasing trends of decision-makers during the evaluation. In the end, the rankings of the three proposed algorithms are compared to identify the most reliable structure.