Novel distance measure for q-rung orthopair fuzzy sets and its application

In this paper, we explore the q-rung orthopair fuzzy set (q-ROFS) framework to effectively manage complex and uncertain information, particularly in scenarios where human opinions and preferences play a critical role. Recognizing the limitations of existing methods in capturing the inherent vagueness of such information, we propose a novel and robust distance measure tailored specifically for q-ROFS. To demonstrate the practical relevance of our proposed measure, we focus on its application to the selection of financial investment funds, a problem characterized by significant uncertainty and subjective evaluation criteria. Within this context, we develop a decision-making algorithm based on the q-ROFS framework. The algorithm leverages the new distance measure to identify an optimal fund from a set of alternatives by analyzing key financial and non-financial attributes. We further validate the effectiveness of our approach by presenting a detailed numerical example that simulates a real-world investment scenario. Comparative analyses with existing decision-making methods are conducted to underscore the improvements in accuracy, flexibility, and overall performance that our approach offers. The results suggest that the proposed q-ROFS-based algorithm is not only capable of handling uncertainty more effectively but also provides enhanced decision-making insights, making it a valuable tool for complex decision-making tasks in finance and beyond.