AI-driven multi-criteria decision-making for advanced materials selection using step fuzzy sets

The fast development of engineering and intelligent manufacturing systems has led to the complexity in advanced materials selection as there can be numerous contradictory criteria, lack of exact information, and subjective opinions of experts involved in the process. Conventional multi-criteria decision-making (MCDM) methods find it difficult to take into account such uncertainty and produce not always reliable results of decision making. In order to solve this problem, this paper suggests an AI-oriented MCDM approach using Step Fuzzy Sets (StepFSs) for the advanced materials selection process. Different traditional fuzzy models which consider only the maximal membership degree, StepFSs allow a flexible rating system choosing the k-th highest membership degree which makes it possible for the decision-maker to reflect different degrees of preference depending on the decision-making situation. The detailed procedure for making decisions using an algorithm is designed, which comprises construction of the StepFS decision matrix, normalization, aggregation, and ranking of material options. In order to verify the efficiency of the proposed approach, the case study of the choice of advanced materials for engineering purposes is provided. The comparative study conducted with the help of the existing methods based on fuzzy MCDM shows that the proposed approach allows more flexible, robust, and accurate ranking of alternatives in the presence of uncertainties. The sensitivity analysis proves the stability of the proposed approach under different decision preferences.