Ajeng Savitri Puspaningrum, Erliyan Redy Susanto, Nirwana Hendrastuty, Setiawansyah Setiawansyah

REFORMULATION OF MULTI-ATTRIBUTE UTILITY THEORY NORMALIZATION TO HANDLE ASYMMETRIC DATA IN MADM

Introduction

Reformulation of multi-attribute utility theory normalization to handle asymmetric data in madm. Reformulates Multi-Attribute Utility Theory (MAUT) normalization with MAUT-A for asymmetric data. Improves ranking accuracy and validity in multi-attribute decision-making (MADM).

Abstract

Multi-Attribute Utility Theory (MAUT) is a widely used multi-attribute decision-making (MADM) method due to its ability to integrate multiple criteria into a single utility value. However, conventional MAUT faces limitations when handling asymmetric data, where standard normalization processes often lead to value distortion and less representative rankings. This study aims to reformulate the normalization function in MAUT to improve adaptability to non-symmetric data distributions and to enhance ranking validity in decision-making. A modification approach called MAUT-A was developed by applying an adaptive normalization mechanism capable of accommodating extreme distributions and outliers by adding Z-score normalization. The performance of MAUT-A was evaluated by comparing the correlation of its ranking results with reference rankings, and the outcomes were benchmarked against conventional MAUT. The experimental findings indicate that conventional MAUT achieved a correlation value of 0.9688 with the reference ranking, while the proposed MAUT-A method achieved a higher correlation of 0.9792. This improvement represents that MAUT-A has better suitability, stability, and reliability in managing asymmetric data. The study contributes by offering a reformulated MAUT framework through adaptive normalization, providing more accurate, stable, and fair ranking outcomes. This approach enhances the validity of MADM applications, particularly in contexts involving asymmetric data distributions

Review

This paper addresses a crucial limitation within Multi-Attribute Utility Theory (MAUT), specifically its challenge in effectively handling asymmetric data distributions. The authors correctly identify that conventional normalization methods employed in MAUT can lead to distorted values and less representative rankings, thereby compromising the reliability and fairness of multi-attribute decision-making (MADM) outcomes. The stated aim to reformulate MAUT's normalization function to improve its adaptability to non-symmetric data and enhance ranking validity is highly relevant and promises to significantly broaden the practical applicability of MAUT. To mitigate this issue, the study introduces MAUT-A, a modified approach that integrates an adaptive normalization mechanism. This novel mechanism is designed to better accommodate extreme distributions and outliers by incorporating Z-score normalization, thus providing a more robust scaling of attribute values. The methodology employed involved evaluating MAUT-A's performance by comparing the correlation of its ranking results with established reference rankings, using conventional MAUT as a direct benchmark. The experimental findings are notably positive, indicating that MAUT-A achieved a higher correlation value of 0.9792, surpassing conventional MAUT's correlation of 0.9688 with the reference ranking. This quantitative improvement strongly suggests MAUT-A's enhanced suitability, stability, and reliability when processing asymmetric data. The core contribution of this research lies in its development of a reformulated MAUT framework through adaptive normalization. By offering a method that yields more accurate, stable, and fair ranking outcomes, the MAUT-A approach significantly enhances the validity and robustness of MADM applications, especially in real-world scenarios often characterized by asymmetric data distributions. This work provides a valuable, empirically supported advancement for researchers and practitioners, offering a practical solution to a recognized weakness of a widely used decision-making paradigm and bolstering confidence in MAUT-based analyses under diverse data conditions.

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