Robustness analysis of a MOEA-based elicitation method for outranking model parameters

Abstract

To set the parameter values for the outranking model is usually a demanding task for the decision-maker (DM) because it is necessary to provide a large number of parameters (thresholds and weights). The use of indirect methods (preference-disaggregation analysis (PDA)) to infer the set of parameters from a battery of decision examples allows the DM to avoid the task of specifying these values. In this paper a robustness analysis has been made to a PDA method based on an evolutionary approach. In this case we use THESEUS method as an artificial DM and its assignment rule for evaluating each individual of a multi-objective evolutionary algorithm (MOEA) population. The non-dominated solutions are acceptable parameter settlements.