Eliciting Correlated Weights for Multi-Criteria Group Decision Making with Generalized Canonical Correlation Analysis

Abstract

The proper solution of a multi-criteria group decision making (MCGDM) problem usually involves a series of critical issues that are to be dealt with, among which two are noteworthy, namely how to assign weights to the (possibly distinct) judgment criteria used by the different decision makers (DMs) and how to reach a satisfactory level of agreement between their individual decisions. Here we present a novel methodology to address these issues in an integrated and robust way, referred to as the canonical multi-criteria group decision making (CMCGDM) approach. CMCGDM is based on a generalized version of canonical correlation analysis (GCCA), which is employed for simultaneously computing the criteria weights that are associated with all DMs. Because the elicited weights maximize the linear correlation between all criteria at once, it is expected that the consensus measured between the DMs takes place in a more natural way, not necessitating the creation and combination of separate rankings for the different groups of criteria. CMCGDM also makes use of an extended version of TOPSIS, a multi-criteria technique that considers the symmetry of the distances to the positive and negative ideal solutions. The practical usefulness of the proposed approach is demonstrated through two revisited examples that were taken from the literature as well as other simulated cases. The achieved results reveal that CMCGDM is indeed a promising approach, being more robust to the problem of ranking irregularities than the extended version of TOPSIS when applied without GCCA.

Keywords

• multi-criteria group decision-making
• generalized canonical correlation analysis
• criteria weights
• consensus measuring
• TOPSIS
• ranking irregularities