AI Summary & Key Takeaways
AI InsightThe proposed methodology can also be adapted to other multi-criteria decision-making problems involving uncertainty and a large number of criteria.
📌 3 Key Takeaways
Abstract
Multi-criteria decision-making problems involving a large number of parameters can lead to information overload. This study proposes a novel parameter reduction framework based on q-rung orthopair fuzzy (qROF) numbers to find the same ranking of alternatives using less parameters. The proposed approach combines two complementary approaches. First, an optimization-based model is developed using score values derived from qROF membership and non-membership degrees to identify the parameters considered without changing the alternative ranking. Second, a qROF-based Minkowski distance measure is introduced to detect redundant parameters based on a predefined threshold value. In current studies, the ranking of alternatives can be partially preserved or parameter reduction can be performed randomly. The proposed approach offers a systematic approach for parameter reduction presenting the same alternative ranking. The applicability of the model is demonstrated through an electric vehicle battery design problem involving multiple criteria and alternatives. The results highlight the effectiveness and robustness of the proposed integrated approach for complex decision-making environments. It successfully reduces the number of parameter while preserving the original ranking of alternatives. The integration of optimization-based and distance-based methods ensures a more clear and interpretable parameter reduction process. The proposed methodology can also be adapted to other multi-criteria decision-making problems involving uncertainty and a large number of criteria.