PhD graduated
Departure date : 08/31/2022

Supervision : Patrice PERNY

Multi-objective optimisation and preference elicitation based on rank-dependant models and reference points

This thesis work falls within the research field of algorithmic decision theory, which is defined at the junction of decision theory, artificial intelligence and operations research. This work focuses on the consideration of sophisticated behaviors in complex decision environments (multicriteria decision-making, collective decision-making and decision under risk and uncertainty). We first propose methods for multi-objective optimization on implicit sets when preferences are represented by rank-dependent models (Choquet integral, bipolar OWA, Cumulative Prospect Theory and bipolar Choquet integral). These methods are based on mathematical programming and discrete algorithmics approaches. Then, we present methods for the incremental parameter elicitation of rank-dependent model that take into account the presence of a reference point in the decision maker's preferences (bipolar OWA, Cumulative Prospect Theory, Choquet integral with capacities and bicapacities). Finally, we address the structural modification of solutions under constraints (cost, quality) in multiple reference point sorting methods. The different approaches proposed in this thesis have been tested and we present the obtained numerical results to illustrate their practical efficiency.

Defence : 05/18/2022

Jury members :

Patrick Meyer, Professeur, IMT Atlantique [rapporteur]
Vincent Mousseau, Professeur, CentraleSupélec [rapporteur]
Christophe Labreuche, Ingénieur recherche et développement, Thalès
Nicolas Maudet, Professeur, Sorbonne Université
Meltem Öztürk, Maître de conférences, Université Paris Dauphine
Patrice Perny, Professeur, Sorbonne Université

Departure date : 08/31/2022

2019-2022 Publications