Abstract:
This paper introduces the TriMetric Fusion (TMF) algorithm, a novel multi-criteria decision-making (MCDM) framework designed to address complex decision problems by integrating three key metrics: the Criteria Aggregated Weighted Index (CAWI), the Balanced Extreme Criteria Index (BECI), and the Euclidean distance — within the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm, enabling a comprehensive evaluation of alternatives based on multiple, often conflicting, criteria. Unlike traditional MCDM methods, TMF ensures stability and consistency by defining the ideal solution based on measurement scales rather than the dataset itself, effectively avoiding the rank reversal problem. To demonstrate its effectiveness, the algorithm was applied to a robotic pathfinding problem, where it successfully balanced competing objectives such as path cost, energy consumption, and distance. The experimental results confirmed that TMF offers a flexible and robust solution for decision-making in complex scenarios, outperforming traditional methods like Dijkstra's algorithm in multi-criteria optimization tasks. Key contributions of this work include the development of a unified framework for handling both maximization and minimization criteria, the introduction of a balanced approach to extreme value consideration, and the demonstration of TMF's computational efficiency and adaptability. While the current implementation has limitations, such as static criteria weights and reliance on normalization techniques, future research directions are proposed to enhance TMF's scalability, integrate machine learning for dynamic weight adjustment, and extend its application to diverse domains. This study highlights TMF's potential as a versatile and powerful tool for MCDM, paving the way for its adoption in real-world decision-making scenarios.
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