TOPSIS method based on a fuzzy covering approximation space: An application to biological nano-materials selection

Abstract

The applications of nanomaterials in biomedicine have received increasing attentions. Therefore, how to choose the most suitable material among a variety of bio-nanomaterials with similar properties is a significant problem. Aiming at this problem, we describe it as a multi-criteria decision-making problem in a fuzzy covering approximation space and solve it via the TOPSIS method. Firstly, combining fuzzy neighborhood operators with fuzzy rough set models, we propose two pairs of covering-based fuzzy rough set models and study the basic properties together with classifications of these two pairs of models. Subsequently, we reveal a new method for determining objective weights using the first pair of covering-based fuzzy rough set models. Combining the objective criteria weight determination method with the TOPSIS method, we provide a solution to solve the problem of bone transplant replacement materials selection. Then, we discuss the experimental effects of our proposed method and the other eight different decision-making methods in practical problems to explain the rationality and enforceability of our approach. Finally, we validate the performance of our approach with the support of ideas of cross-validation and hypothesis testing.