ORKAD team

Operational Research, Knowledge And Data

Leader: Laetitia Jourdan



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.

Camille Pageau

Optimization for automatic design and cofiguration of datamining algorithms : Application to medical data

Laurent Parmentier

MO-AutoML : un framework multiobjectif pour la configuration automatique d'algorithmes de machine learning

Weerapan Sae-dan

Automatic Design of Dynamic Local Search Algorithms

Rabin Kumar Sahu

Méthodes d'optimisation adaptatives pour l'optilisation de la chaîne d'approvisionnement

Anne-lise Bedenel

Appariement de descripteurs évoluant dans le temps. Application à la comparaison d'assurance 2019-04-03

Lucien Mousin

Extraire et exploiter la connaissance pour mieux optimiser 2018-11-28

Aymeric Blot

Reacting and Adapting to the Environment – Designing Autonomous Methods for Multi-Objective Combinatorial Optimisation 2018-09-21

Sylvain Dufourny

Optimizing competitive economic decisions in a business game 2017-10-13

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