Factor analysis, sparse PCA, and Sum of Ranking Differences-based improvements of the Promethee-GAIA multicriteria decision support technique

The Promethee-GAIA method is a multicriteria decision support technique that defines the aggregated ranks of multiple criteria and visualizes them based on Principal Component Analysis (PCA). In the case of numerous criteria, the PCA biplot-based visualization do not perceive how a criterion influences the decision problem. The central question is how the Promethee-GAIA-based decision-making process can be improved to gain more interpretable results that reveal more characteristic inner relationships between the criteria. To improve the Promethee-GAIA method, we suggest three techniques that eliminate redundant criteria as well as clearly outline, which criterion belongs to which factor and explore the similarities between criteria. These methods are the following: A) Principal factoring with rotation and communality analysis (P-PFA), B) the integration of Sparse PCA into the Promethee II method (P-SPCA), and C) the Sum of Ranking Differences method (P-SRD). The suggested methods are presented through an 14.0+ dataset that measures the Industry 4.0 readiness of NUTS 2-classified regions. The proposed methods are useful tools for handling multicriteria ranking problems, if the number of criteria is numerous.

Automated summary

The Promethee-GAIA method is a multicriteria decision support technique that provides aggregated ranks and visualization of multiple criteria. This study proposes three techniques to improve the interpretability of the method by eliminating redundant criteria, identifying criterion-factor associations, and exploring similarities between criteria. These techniques are useful tools for handling multicriteria ranking problems with numerous criteria.