Efficient Weight Ranking in Multi-Criteria Decision Support Systems

Lakmayer, S. & Danielson, M. (2025). Efficient Weight Ranking in Multi-Criteria Decision Support Systems. Electronics 14 (7) p. 1237. 10.3390/electronics14071237.

Preview

Text electronics-14-01237.pdf - Published Version Available under License Creative Commons Attribution. Download (12MB) | Preview

Abstract

There are well-known issues in conjunction with eliciting probabilities, utilities, and criteria weights in real-life decision analysis. This article explores various computationally efficient methods for generating weights in multi-criteria decision support systems. Therefore, it constitutes an aid for MCDA modellers and tool designers in selecting surrogate methods for criteria weights. Given the challenges in eliciting precise criteria weights from decision-makers, this study evaluates a range of techniques for automatically generating surrogate weights, focusing on both ordinal and cardinal ranking approaches. With a thorough inquiry methodology never before used, we examine automatic multi-criteria weight-generating algorithms in this article. The methods tested include traditional rank-based models such as rank sum (RS), rank reciprocal (RR), and rank order centroid (ROC), alongside newer approaches like the sum reciprocal (SR) and cardinal sum reciprocal (CSR). The results show that the SR approach for the ordinal case and the CSR method for the cardinal case perform better in terms of robustness than other methods, even including the promising new geometric class of methods. It is also shown that linear programming (LP) performs poorly when compared to surrogate weight models. Additionally, as expected, the cardinal models perform better than the ordinal models. Unexpectedly, though, the well-established LP model’s performance is worse than previously thought.

Actions (login required)

View Item