Qualitative AHP Models under the Belief Function Framework

Amel Ennaceur (1) , Zied Elouedi (1) , Eric Lefevre (2)

1 LARODEC - Laboratoire de Recherche Opérationnelle de Décision et de Contrôle de Processus
2 LGI2A - Laboratoire de Génie Informatique et d'Automatique de l'Artois

This paper investigates a multi-criteria decision making method in an uncertain environment, where the un certainty is represented using the belief function framework. In this context, we suggest a novel methodology that tackles the challenge of introducing uncertainty in the expert evaluations. Therefore, the Analytic Hierarchy Process with qualitative belief function framework is adopted to get numeric repre sentation of qualitative assessment. In this work, we will also focus on two AHP extensions under qualitative AHP. Besides, we intend to describe some comparisons on the standard AHP and the presented models to judge their accuracy. We use also a simulation approach to compare the results of the different models based on different matrices dimensions.

Mots clés: AHP; Belief function theory; Decision making; Uncertainty; Preference relations; AHP

Domaines: Intelligence artificielle [cs.AI]

Amel Ennaceur, Zied Elouedi, Eric Lefevre. Qualitative AHP Models under the Belief Function Framework. 2015 15th International Conference on Intelligent Systems Design and Applications (ISDA), Dec 2015, Marrakech, Morocco. pp.128-133, ⟨10.1109/ISDA.2015.7489213⟩. ⟨hal-03649496⟩