LAUSR

Today, Molodtsov's soft set has been generalised to hypersoft sets, and the use of hypersoft set theory for uncertain data has become more preferable than soft sets. However, the membership degree of an object in hypersoft sets is 0 or 1. In order to express this situation in the range , many mathematical models have been constructed by considering hypersoft sets together with fuzzy sets and their derivatives. However, these mathematical models require the decision-maker to express the membership degrees. It is a very difficult task for a decision-maker to determine a value in and the probability of an error is very high. For this reason, the concepts relational hypersoft membership degree and inverse relational hypersoft membership degree, which are given less dependent on decision-makers, are proposed in this paper. Moreover, two decision-making algorithms are given to use these concepts in an environment of uncertainty. Finally, the decision-making process for the given algorithms is analysed.