APPROACHES TO LENIENCY REDUCTION IN MULTI-CRITERIA DECISION MAKING WITH INTERVAL-VALUED FUZZY SETS AND AN EXPERIMENTAL ANALYSIS

The purpose of this paper is to present a useful method for estimating the importance of criteria and reducing the leniency bias in multiple criteria decision analysis based on interval-valued fuzzy sets. Several types of net predispositions are defined to represent an aggregated effect of interval-valued fuzzy evaluations. The suitability function for measuring the overall evaluation of each alternative is then determined based on simple additive weighting (SAW) methods. Because positive or negative leniency may exist when most criteria are assigned unduly high or low ratings, respectively, deviation variables are introduced to mitigate the effects of overestimated and underestimated ratings of criterion importance. Considering the two objectives of maximal weighted suitability and minimal deviation values, an integrated programming model is proposed to compute optimal weights for the criteria and corresponding suitability degrees for alternative rankings. A flexible algorithm using interval-valued fuzzy SAW methods is established by considering both objective and subjective information to compute optimal multiple criteria decisions. The feasibility and effectiveness of the proposed methods are illustrated by a numerical example. Finally, an experimental analysis of interval-valued fuzzy rankings given different conditions for the criterion weights is conducted with discussions on average Spearman correlation coefficients and contradiction rates.