Flexible fuzzy reinforcement learning MCDM approach for bioprinter selection through resolving criteria dependencies

Abstract

Selecting a suitable bioprinter remains a challenging task involving conflicting criteria that are difficult to weigh. Existing fuzzy multi-criteria decision-making (MCDM) methods fail to derive the fuzzy priorities of criteria while maintaining efficiency, and it is also difficult to consider the dependencies among the criteria. This study aims to solve these problems by proposing a flexible fuzzy reinforcement learning MCDM (FFRL-MCDM) approach. In the FFRL-MCDM, first, a nonlinear calibrated fuzzy reinforcement learning (ncFRL) method is devised to accurately and efficiently derive the fuzzy priorities of criteria. Subsequently, realistically feasible reference points are defined by considering dependencies between criteria to modify the fuzzy technique for order preference by similarity to the ideal solution (FTOPSIS) approach. The proposed FFRL-MCDM approach has been applied to a real case in the literature. Experimental results showed that the proposed FFRL-MCDM approach improved the accuracy of deriving fuzzy priorities with extremely high efficiency. Furthermore, the overall performances and ranks of the evaluated bioprinters did change by comparing to more realistic reference points. However, in this case, the scope of the dependency treatment was restricted to pairwise linear dependencies.