A Novel Multi-Attribute Decision-Making Method for Supplier Selection in the Health Care Industry Using Cosine Similarity Measures of Single-Valued Neutrosophic Cubic Hypersoft Sets

Cosine similarity measures are essential in situations that assess the similarities and differences between two potential outcomes. For different extensions of fuzzy sets, soft sets, and hypersoft sets, a wide range of similarity metrics have been examined in the literature. On the other hand, decision-making problems like sustainable supply chain management, in a single-valued neutrosophic cubic hypersoft set (svNCHSS) scenario have not been addressed so far previously using similarity metrics. Improved cosine similarity measures of svNCHSSs based on the cosine function are proposed by combining the cosine similarity measures of simplified neutrosophic sets in vector space that are currently available. It also looks into their properties and discusses their problems. In the svNCHSS environment, we developed a multi-attribute strategy for evaluating sustainable supplier selection (SuSS) in the healthcare sector using cosine and weighted cosine similarity measures. To construct the ranking order of these values and opt for the most appropriate supplier, the method entails calculating the similarity measure values between each assessed supplier and the ideal supplier. Four options are ranked based on thirteen sustainability-related sub-criteria that are connected to each of the three primary criteria like economic, social, and environmental that take into account all aspects of sustainability in a case study of a SuSS procedure for the healthcare sector. The results of the ranking technique are strongly influenced by the weights given to the attributes. For a given circumstance, no ranking method can ensure trustworthy selection. By contrasting their results with current similarity measures, the suggested decision-making problems involving many criteria and their sub-criteria are verified.