Interactive Multiobjective Optimization Using a Set of Additive Value Functions

Authors:
José Rui Figueira;Salvatore Greco;Vincent Mousseau;Roman Słowiński
Affiliations:
CEG-IST, Center for Management Studies, Instituto Superior Técnico, Technical University of Lisbon, Portugal;Faculty of Economics, University of Catania, Catania, Italy 95129;LAMSADE, Université Paris-Dauphine, Paris, France 75775;Institute of Computing Science, Poznań University of Technology, Poznań, Poland 60-965 and Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland 01-447
Venue:
Multiobjective Optimization
Year:
2008

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Abstract

In this chapter, we present a new interactive procedure for multiobjective optimization, which is based on the use of a set of value functions as a preference model built by an ordinal regression method. The procedure is composed of two alternating stages. In the first stage, a representative sample of solutions from the Pareto optimal set (or from its approximation) is generated. In the second stage, the Decision Maker (DM) is asked to make pairwise comparisons of some solutions from the generated sample. Besides pairwise comparisons, the DM may compare selected pairs from the viewpoint of the intensity of preference, both comprehensively and with respect to a single criterion. This preference information is used to build a preference model composed of all general additive value functions compatible with the obtained information. The set of compatible value functions is then applied on the whole Pareto optimal set, which results in possible and necessary rankings of Pareto optimal solutions. These rankings are used to select a new sample of solutions, which is presented to the DM, and the procedure cycles until a satisfactory solution is selected from the sample or the DM comes to conclusion that there is no satisfactory solution for the current problem setting. Construction of the set of compatible value functions is done using ordinal regression methods called UTA$^{\mbox{\scriptsize GMS}}$ and GRIP. These two methods generalize UTA-like methods and they are competitive to AHP and MACBETH methods. The interactive procedure will be illustrated through an example.