index - Bibliographie de l’équipe ORKAD

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

Optimization Coverage path planning Algorithmes de recherche locale University timetabling Multi-Objective Optimisation Analyse de paysages de recherche Contrôle adaptatif Delay-constrained minimum spanning tree Timetabling Automation Performance prediction Augmented ϵ-constraint Combinatorial optimization Configuration automatique des algorithmes Domain Ontology Recherche opérationnelle Local search Bi-objective optimization Approvisionnement multi article Neural architecture search Algorithm Configuration Fitness Landscapes Automatic Algorithm Configuration Multi-Objective Optimization Data mining Local Optima Networks Automatic configuration Constrained minimum spanning tree problems Augmented ϵ -constraint Assurance Routing Classification Algorithms Classification Recherche locale Algorithme génétique Combinatorial optimization Automatic algorithm configuration Dynamic programming core model Metaheuristics Métaheuristiques Déconstruction sélective CCS Concepts • Applied computing → Multi-criterion optimization and decision-making Bounded archive Deep Learning WEB Approvisionnement multi-échelon Bayesian dynamic network Temporal data Clique Biclustering Clustering Bottleneck objective function Multi-objective local search Machine learning Bidimensionality Combinatorial Optimisation Dynamic programming Clarke & Wright MOEA/D Combinatorial optimisation Configuration automatique Co-clustering Knowledge Discovery Routing Problems Local Search Design automatique d’algorithmes BIC Adaptive Mechanisms Cluster Editing/Deletion/Completion problems Multi-objective Optimisation combinatoire Configuration automatique d'algorithmes Disaster relief Digital learning Adaptive Control Automatic configuration tuning Algorithm Selection Multi-objective optimization Gestion des stocks Complexity Machine Learning CVRP Compromis exploration-exploitation Landscape analysis Drone swarms routing multi-agents systems humanitarian logistics disaster relief Approvisionnement Multi-objective optimisation Complexity dichotomy Automatic Configuration Corporate Algorithmes évolutionnaires multiobjectifs Treewidth Neural Architecture Search Heuristics Graph algorithm Drone swarms Bi-objective Combinatorial Optimization Automatic algorithm design Operations research Agent-based model