Combining analytical hierarchy process and Choquet integral within non-additive robust ordinal regression

• We use the Choquet integral and the Non-Additive Robust Ordinal Regression (NAROR) to consider interaction between criteria.
• We combine the Choquet integral preference model and the Analytic Hierarchy Process (AHP).
• We use AHP to build a common scale for the considered criteria.
• We reduce the cognitive effort of AHP considering only representative values on each criterion.
• We adopt NAROR to take into account all non-additive weights compatible with preference information.

We consider multiple criteria decision aiding in the case of interaction between criteria. In this case the usual weighted sum cannot be used to aggregate evaluations on different criteria and other value functions with a more complex formulation have to be considered. The Choquet integral is the most used technique and also the most widespread in the literature. However, the application of the Choquet integral presents two main problems being the necessity to determine the capacity, which is the function that assigns a weight not only to all single criteria but also to all subset of criteria, and the necessity to express on the same scale evaluations on different criteria. While with respect to the first problem we adopt the recently introduced Non-Additive Robust Ordinal Regression (NAROR) taking into account all the capacities compatible with the preference information provided by the DM, with respect to the second one we build the common scale for the considered criteria using the Analytic Hierarchy Process (AHP). We propose to use AHP on a set of reference points in the scale of each criterion and to use an interpolation to obtain the other values. This permits to reduce considerably the number of pairwise comparisons usually required by the DM when applying AHP. An illustrative example details the application of the proposed methodology.