Bonferroni-based aggregation operators and decision-making framework under complex fuzzy hypersoft sets for selection of smart healthcare systems

Abstract

In recent years, the increasing uncertainty and complexity in real-life decision-making (DM) problems have made necessary the establishment of more reliable and flexible mathematical structures. Classical aggregation operators (AOs) are not capable to model interrelationships among decision attributes, which may result to less reliable decision rankings. To handle these limitations, this paper establishes innovative Bonferroni operational laws within the environments of complex fuzzy hypersoft sets (CFHS-Sets). Based on these laws, family of Bonferroni mean operators (BMOs) are constructed, capable of handling interrelationships among decision criteria through adjustable control parameters. The key characteristics and fundamental properties of the newly defined BMOs such as monotonicity, idempotency, and boundness, are methodically examined to verify their mathematical authenticity and applicability. Moreover, an approach of multi-criteria decision-making (MCDM) is designed based on the proposed aggregation mechanisms and BMOs within the environments of CFHS-Sets. This MCDM approach is employed to select a best smart healthcare system, which shows its effectiveness and practicality of the method. Furthermore, a comparative analysis with existing fuzzy aggregation models is conducted to validate the robustness and superiority. The final outcomes reflect that the proposed approaches provide more reliable, stable, and flexible decision rankings by integrating the parameters tuples and interdependency among decisions attributes. Thus, the proposed approaches contribute a generalized and powerful tool for solving complex DM problems and can be modified to other real-life applications involving parameters tuples and interdependent criteria.