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

Salvatore Corrente, Michael Doumpos, Salvatore Greco, Roman Słowiński, Constantin Zopounidis

Research output: Contribution to journalArticlepeer-review

214 Downloads (Pure)

Abstract

A hierarchical decomposition is a common approach for coping with complex decision problems in-volving multiple dimensions. Recently, the Multiple Criteria Hierarchy Process (MCHP) has been introduced as a new general framework for dealing with multiple criteria decision aiding (MCDA) 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 comprehen- sive 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.
Original languageEnglish
Pages (from-to)117-139
JournalAnnals of Operations Research
Volume251
Issue number1
Early online date28 May 2015
DOIs
Publication statusPublished - Apr 2017

Keywords

  • Multiple criteria decision aiding
  • Multiple criteria hierarchy process
  • Sorting problems
  • Robust ordinal regression
  • Bank rating

Fingerprint

Dive into the research topics of 'Multiple criteria hierarchy process for sorting problems based on ordinal regression with additive value functions'. Together they form a unique fingerprint.

Cite this