Wassim Bouazza
- Laboratoire des Sciences du Numérique de Nantes (LS2N)
- Conception, Pilotage, Surveillance et Supervision des systèmes (LS2N - équipe CPS3)
- Nantes Université (Nantes Univ)
Présentation
Wassim BOUAZZA est Maître de Conférences (MCF) en informatique et génie industriel, affilié au laboratoire LS2N CNRS (UMR 6004) à Nantes Université, où il enseigne au sein du département Qualité, Logistique Industrielle et Organisation de l'IUT de Nantes. Titulaire d'un doctorat en Sciences Informatiques obtenu à l'Université Oran 1, il se spécialise dans des domaines tels que l'Industrie 4.0, l'intelligence artificielle, les hyper-heuristiques, les systèmes de production intelligents et les systémes de transport intelligents et collaboratifs.
Compétences
Publications
Publications
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Machine Learning prediction model for Dynamic Scheduling of Hybrid Flow-Shop based on MetaheuristicIFAC-PapersOnLine, 2024, 58 (19), pp.1228-1233. ⟨10.1016/j.ifacol.2024.09.077⟩ |
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Dynamic scheduling of manufacturing systems: a product-driven approach using hyper-heuristicsInternational Journal of Computer Integrated Manufacturing, 2021, 34 (6), pp.641-665. ⟨10.1080/0951192X.2021.1925969⟩ |
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Effective dynamic selection of smart products scheduling rules in FMSManufacturing Letters, 2019, 20, pp.45-48. ⟨10.1016/j.mfglet.2019.05.004⟩ |
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L'énumération partielle dans la prédiction en ordonnancement dynamique d'un flowshop hybride3ème congrès annuel de la SAGIP, May 2025, Mulhouse (Haut-Rhin), France |
Integrating Sustainability with Equitable Operator Fatigue DistributionAdvances in Production Management Systems, Sep 2025, Kamakura, Japan. pp.262-279, ⟨10.1007/978-3-032-03546-2_18⟩ |
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Simulation Stochastique et Gamification : Une Nouvelle Approche de la Formation Industrielle3ème Congrès Annuel de la Société d'Automatique, de Génie Industriel et de Productique (SAGIP 2025), May 2025, Mulhouse, France |
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Exploring the Partial Enumeration Problem for Dynamic Hybrid Flowshop Scheduling with Machine Learning11th IFAC Conference on Manufacturing Modelling, Management and Control – IFAC MIM2025, Jun 2025, Trondheim, Norway. pp.915-920, ⟨10.1016/j.ifacol.2025.09.155⟩ |
Machine Learning-Based Hyper-Heuristics: A Clear InsightCIIS 2024: 2024 The 7th International Conference on Computational Intelligence and Intelligent Systems, Nov 2024, Nagoya Japan, France. pp.29-37, ⟨10.1145/3708778.3708783⟩ |
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Forecast-Driven Reconfiguration in Sustainable Production SystemsIFIP APMS, Aug 2025, Kamakura, Japan. pp.246-261, ⟨10.1007/978-3-032-03546-2_17⟩ |
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Apprentissage automatique pour l'ordonnancement dynamique d'un atelier de production hybride basé sur une métaheuristique2ème Congrès Annuel de la Société d'Automatique, de Génie Industriel et de Productique (SAGIP 2024), May 2024, Villeurbanne (France), France |
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A Hyper-heuristic for Dynamic Scheduling of Cyber-Physical Production Systems Using Incremental LearningService Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future. SOHOMA 2023, 2023, Annecy, France. pp.200-211, ⟨10.1007/978-3-031-53445-4_17⟩ |
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Machine Learning prediction model for Dynamic Scheduling of Hybrid Flow-Shop based on MetaheuristicINCOM 2024, Aug 2024, Vienne (AUT), France |
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Hyper-heuristics applications to manufacturing scheduling: overview and opportunitiesIFAC World Congress, IFAC, Jul 2023, Yokoama, Japan. pp.935 - 940, ⟨10.1016/j.ifacol.2023.10.1685⟩ |
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Review of smart cyber city: keys requirements, tools and issues22nd IFAC World Congress, 2023, Yokohama, Japan. pp.8189-8196, ⟨10.1016/j.ifacol.2023.10.999⟩ |
Toward Efficient FMS Scheduling Through Rules Combination Using an Optimization-Simulation MechanismSOHOMA’21 : 11th International Workshop on Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future, Nov 2021, Cluny, France. pp.559-571, ⟨10.1007/978-3-030-99108-1_40⟩ |
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Environmental impacts of the Collaborative Intelligent Transport Systems (C-ITS): survey and research directionsGIN Conference on Logistics | New Technologies & Effective Circular Economy, LAMIH (Laboratory of industrial and human automation, mechanics and computer science); UPHF (Université Polytechnique Hauts-de-France); GIN (the Greening of Industry Network), Jul 2022, Valenciennes, France |
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A distributed approach solving partially flexible job-shop scheduling problem with a Q-learning effect20th World Congress of the International Federation of Automatic Control, Jul 2017, Toulouse, France. pp.15890-15895, ⟨10.1016/j.ifacol.2017.08.2354⟩ |
Sustainable Scheduling in Manufacturing: Integrating NSGA-II and Discrete-Event Simulation for Hybrid Flowshop EnvironmentsCIIS 2024: 2024 The 7th International Conference on Computational Intelligence and Intelligent Systems, 2024, Nagoya, Japan. ACM, pp.94-103, 2025, ⟨10.1145/3708778.3708792⟩ |
A Model for Manufacturing Scheduling Optimization Through Learning Intelligent ProductsService Orientation in Holonic and Multi-agent Manufacturing, 594, Springer International Publishing, pp.233-241, 2015, Studies in Computational Intelligence, ⟨10.1007/978-3-319-15159-5_22⟩ |
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Proposition d’une méthode d’apprentissage à base de produits intelligents pour les ateliers partiellement flexibles / dynamiquesAutomatique. Université Oran 1 - Ahmed Ben Bella, 2020. Français. ⟨NNT : ⟩ |