ORKAD Team - Operational Research, Knowledge And Data

ORKAD is a research team of the thematic group OPTIMA from CRIStAL (Centre de Recherche en Informatique, Signal et Automatique de Lille) Laboratory (UMR CNRS 9189).

Tthe 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.

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Keywords

Configuration automatique des algorithmes Compression de graphes Inventory Management Heuristics Combinatorial Optimisation Operations research Agent -based modelling Bayesian dynamic network Automation WEB Gestion des stocks Configuration automatique Apprentissage machine Bi-objective Optimisation multi-objectif Performance prediction Classification Algorithms Co-clustering Déconstruction sélective Métaheuristique MOEA/D Recherche opérationnelle Adaptive Control Temporal data Automatic Configuration Classification Combinatorial optimization Machine Learning Graph algorithm Bounded archive Fitness Landscapes Assurance Bidimensionality Adaptive Mechanisms Multi-Objective Optimisation University timetabling Dynamic programming Machine learning Combinatorial Optimization Automatic algorithm configuration Approvisionnement Multi-Objective Optimization Biclustering Multi-objective Multi-objective optimization Algorithmes évolutionnaires multiobjectifs Routing Problems Metaheuristic Cluster Editing/Deletion/Completion problems Metaheuristics Neural architecture search Analyse de paysage d’optimisation Clarke & Wright Optimization Routing Combinatorial optimisation Local search Knowledge Discovery Multi-objective local search BIC Configuration automatique d'algorithmes Analyse de paysages de recherche Clustering CVRP Automatic Algorithm Configuration Algorithmes de recherche locale Local Search Complexity dichotomy Parameterized complexity Bottleneck objective function Constrained minimum spanning tree problems Local Optima Networks Algorithme génétique Automatic algorithm design Selective deconstruction Approvisionnement multi-échelon Corporate Bi-objective optimization Approvisionnement multi article Augmented ϵ-constraint Combinatorial optimization Applications industrielles Compromis exploration-exploitation Augmented ϵ -constraint Clique Recherche locale Treewidth Neural Architecture Search Optimisation combinatoire Multi-objective optimisation Timetabling Complexity Agent-based model Complexité paramétrée Algorithm Selection Landscape analysis Automatic configuration Contrôle adaptatif Métaheuristiques Algorithm Configuration CCS Concepts • Applied computing → Multi-criterion optimization and decision-making