Multiple criteria hierarchy process for sorting problems based on ordinal regression with additive value functions

A hierarchical decomposition is a common approach for coping with complex decision problems involving multiple dimensions. Recently, the multiple criteria hierarchy process (MCHP) has been introduced as a new general framework for dealing with multiple criteria decision aiding in case of a hierarchical structure of the family of evaluation criteria. This study applies the MCHP framework to multiple criteria sorting problems and extends existing disaggregation and robust ordinal regression techniques that induce decision models from data. The new methodology allows the handling of preference information and the formulation of recommendations at the comprehensive level, as well as at all intermediate levels of the hierarchy of criteria. A case study on bank performance rating is used to illustrate the proposed methodology.