Preference elicitation from inconsistent judgments using multi-objective optimization

Several decision-making techniques involve pairwise comparisons to elicit the preferences of a decision maker (DM). This paper proposes a new approach for prioritization from pairwise comparisons using the concept of indirect judgments. No method exists that simultaneously minimizes deviations from both direct and indirect judgments. In order to estimate preferences, it is sensible to consider both the acquired judgments and the other judgments latent in the DM’s mind. Hence, a technique is developed here to minimize the deviations from both types of judgments. Estimated preferences are generally evaluated based on two criteria: their deviation from the provided judgments and the number of judgments that have been ordinarily violated. Here, it is proposed to optimize three objectives simultaneously: the deviations from both direct and indirect judgments, and the number of judgments violated. A prototype application has been developed to generate all non-dominated solutions using a multiple-objective evolutionary algorithm. The new approach is shown to offer users greater flexibility than all other tested methods.