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Word cloudA word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
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Bourdin, M., Neumann, A., Paviot, T., Pellerin, R., & Lamouri, S. (2025). A Framework for Selecting the Optimal NLP Solution for Classification Tasks in Industry 4.0 Based on Data and Business Constraints. IFAC-PapersOnLine, 59(10), 1850-1855. Presented at 11th IFAC Conference on Manufacturing Modelling, Management and Control (MIM 2025), Trondheim, Norway. External link
Bourdin, M., Neumann, A., Paviot, T., Pellerin, R., & Lamouri, S. (2024). Exploring the applications of natural language processing and language models for production, planning, and control activities of SMEs in industry 4.0: a systematic literature review. Journal of Intelligent Manufacturing, 21 pages. External link
Neumann, A., Angela Cremona, M., Haji, A., Morin, M., & Rekik, M. (2022, June). Exploring the Recent Applications of Artificial Intelligence Techniques for Type-1 Diabetes Management [Paper]. 2nd Francophone summer school on health service management (SSHSM 2022), Québec, Québec, Canada. Published in Springer proceedings in mathematics & statistics. External link
Neumann, A., Hajji, A., Rekik, M., & Pellerin, R. (2025). I4Evosim: An Educational Platform Simulating a Competitive ETO Market. IFAC-PapersOnLine, 59(10), 1101-1106. External link
Neumann, A., Zghal, Y., Cremona, M. A., Hajji, A., Morin, M. J., & Rekik, M. (2025). A data-driven personalized approach to predict blood glucose levels in type-1 diabetes patients exercising in free-living conditions. Computers in Biology and Medicine, 190, 110015 (27 pages). Available
Neumann, A., Hajji, A., Rekik, M., & Pellerin, R. (2024). Integrated planning and scheduling of engineer-to-order projects using a Lamarckian Layered Genetic Algorithm. International Journal of Production Economics, 267, 109077 (17 pages). External link
Neumann, A., Hajji, A., Rekik, M., & Pellerin, R. (2023). Genetic algorithms for planning and scheduling engineer-to-order production: a systematic review. International Journal of Production Research, 30 pages. External link
Neumann, A., Rekik, M., Morin, M., Hajji, A., & Pellerin, R. (2023, June). Ordonnancement dynamique et contrôle intelligent d'un robot industriel [Paper]. CIGI Qualita MOSIM 2023, Trois-Rivières, Qc, Canada. External link
Neumann, A., Hajji, A., Rekik, M., & Pellerin, R. (2022, June). A Didactic Review On Genetic Algorithms For Industrial Planning And Scheduling Problems [Abstract]. 10th IFAC Triennial Conference on Manufacturing Modelling, Management and Control (MIM 2022), Nantes, France. Published in IFAC PapersOnLine, 55(10). External link
Neumann, A., Hajji, A., Rekik, M., & Pellerin, R. (2022). Integrated planning and scheduling of engineer-to-order projects using a Lamarckian layered genetic algorithm. (Technical Report n° CIRRELT-2022-35). Unavailable
Neumann, A., Hajji, A., Rekik, M., & Pellerin, R. (2022). A model for advanced planning systems dedicated to the Engineer-To-Order context. International Journal of Production Economics, 252, 16 pages. External link
Neumann, A., Hajji, A., Rekik, M., & Pellerin, R. (2022, June). A Two-Level Optimization Approach For Engineer-To-Order Project Scheduling [Abstract]. 10th IFAC Triennial Conference on Manufacturing Modelling, Management and Control (MIM 2022), Nantes, France. Published in IFAC PapersOnLine, 55(10). External link
