AURA: An Adaptive Utility Ranking Algorithm for Multi-Criteria Decision Making with Python-based decision support interface

This paper presents the Adaptive Utility Ranking Algorithm (AURA), a Python-based software tool designed for solving Multi-Criteria Decision-Making (MCDM) problems with flexibility, efficiency, and scalability. AURA introduces a novel adaptive distance-based scoring mechanism that unifies benefit, cost, and target-type criteria into a single, interpretable formula, while allowing reference points, ideal, anti-ideal, and average to be dynamically defined. This enhances robustness in handling real-world decision problems, including criteria with narrow ranges or skewed distributions. The method has been benchmarked against established MCDM methods such as TOPSIS and VIKOR, demonstrating high ranking consistency through correlation analysis. Performance tests on large-scale datasets (up to 1,000 alternatives × 20 criteria) further confirm its computational efficiency and scalability. The software includes an intuitive Streamlit-based graphical interface with Excel input/output support and visualization tools, making it suitable for academic, industrial, and big data applications. The open-source implementation supports rapid experimentation, reproducibility, and extensibility for advanced MCDM research and practice.