The ORKAD team aims to exploit simultaneously expertise in combinatorial optimization and knowledge extraction to address upcoming optimization problems. While the two scientific areas are developed more or less independently, the synergy between combinatorial optimization and knowledge extraction offers an opportunity first, to improve the performance and autonomy of optimization methods thanks to Knowledge and secondly to solve efficiently Knowledge extraction problems thanks to operational research methods. Our approaches are mainly based on mono and multi-objective combinatorial optimization and led to the development of open source software.
Laetitia Jourdan
Online Algorithms for Graph Summarization
Intégration de machine Learning pour la résolution de MO-VRPTWs avec applications dans l'environnement hospitalier
Modélisation, simulation et optimisation d'une supply chain multi-échelon
Parameterized complexity : A tool for multiobjective problems. Application to lung cancer detection
Multi-Configuration Automatique d'Algorithmes pour l'Optimisation Combinatoire 20/06/2022
Optimizing competitive economic decisions in a business game 13/10/2017