Managing critical tank reversals in TOPSIS: A mathematical framework for ensuring stable ideal solutions

This research examines the impact of non-dominated alternatives on rankings and explores rank reversal (RR) in the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a widely used distance-based MCDM method. A theoretical analysis reveals how the mathematical operations of TOPSIS contribute to RR through the relative closeness and separation measures. Four scenarios are outlined to identify conditions where RR becomes unavoidable. The study provides new insights into the mathematical foundations of RR and its implications for decision makers. To address this issue, three strategies are proposed: identifying non-dominated alternatives, recognizing conditions leading to close performance margins, and normalizing ideal solutions to fixed reference values. These findings offer practical guidance for developing distance-based MCDM methods that minimize rank reversal.