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Informatica


Resolving Rank Reversal in TOPSIS: A Comprehensive Analysis of Distance Metrics and Normalization Methods
Volume 35, Issue 4 (2024), pp. 837–858
Pub. online: 14 November 2024      Type: Research Article      Open accessOpen Access
Received
1 June 2024
Accepted
1 October 2024
Published
14 November 2024

Abstract

This paper examines ranking reversal (RR) in Multiple Criteria Decision Making (MCDM) using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Through a mathematical analysis of min-max and max normalization techniques and distance metrics (Euclidean, Manhattan, and Chebyshev), the study explores their impact on RR, particularly when new, high-performing alternatives are introduced. This research provides insight into the causes of RR, offering a framework that clarifies when and why RR occurs. The findings help decision-makers select appropriate techniques, promoting more consistent and reliable outcomes in real-world MCDM applications.

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Keywords
ranking reversal TOPSIS normalization distance metric extreme alternative

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