A metaheuristic for inferring a ranking model based on multiple reference profiles

Arwa Khannoussi (1, 2) , Alexandru-Liviu Olteanu (3, 4) , Patrick Meyer (3, 5) , Bastien Pasdeloup (5, 6)

1 IMT Atlantique - DAPI - Département Automatique, Productique et Informatique
2 LS2N - équipe MODELIS - Modélisation, Optimisation et DEcision pour la Logistique, l'Industrie et les Services
3 Lab-STICC_DECIDE - Equipe DECIDE
4 UBS - Université de Bretagne Sud
5 IMT Atlantique - LUSSI - Département Logique des Usages, Sciences sociales et Sciences de l'Information
6 Lab_STICC_BRAIn - Equipe Better Representations for Artificial Intelligence

In the context of Multiple Criteria Decision Aiding, decision makers often face problems with multiple conflicting criteria that justify the use of preference models to help advancing towards a decision. In order to determine the parameters of these preference models, preference elicitation makes use of preference learning algorithms, usually taking as input holistic judgments expressed by the decision maker. Tools to achieve this goal in the context of a ranking model based on multiple reference profiles are usually based on mixed-integer linear programming or constraint programming. However, they are usually unable to handle realistic problems involving many criteria and a large amount of input information. We propose here an evolutionary metaheuristic in order to address this issue. Extensive experiments illustrate its ability to handle problem instances that previous proposals cannot.

Keywords: multi-criteria decision aiding; preference elicitation and learning; ranking problem; reference profiles; meta-heuristic

Domains: Operations Research [math.OC]

Arwa Khannoussi, Alexandru-Liviu Olteanu, Patrick Meyer, Bastien Pasdeloup. A metaheuristic for inferring a ranking model based on multiple reference profiles. Annals of Mathematics and Artificial Intelligence, 2024, ⟨10.1007/s10472-024-09926-w⟩. ⟨hal-04017642v2⟩