Monte Carlo-Based Interval TOPSIS for Navigating Decision Support Under Uncertainty

Jingda Ying, Christian Wagner, Isaac Triguero, Shaily Kabir

Abstract: Multi-criteria decision-making (MCDM) techniques are widely used to facilitate systematic and informed decision making. However, real-world problems in diverse fields such as business, cybersecurity, and environmental management often involve significant levels of uncertainty. While progress has been made in developing MCDM methods capable of addressing uncertainty, many challenges remain–particularly in capturing uncertainty, communicating it effectively to stakeholders, and selecting the most suitable MCDM technique for a given context. This paper focuses on interval extensions of the widely used Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), highlighting the need to (1) effectively communicate uncertainty to decision makers and (2) establish a way to compare these extensions correctly. To address these, we propose a Monte Carlo-based approach for interval TOPSIS, offering a new interval extension of the original TOPSIS to handle uncertainty independently. Meanwhile, we leverage a data set from the literature and use the proposed extension as a reference point to compare some of the most recent TOPSIS extensions and analyse their underlying differences in uncertainty handling.

External IDs:dblp:conf/ideal/YingWTK25