Open Access
Issue
MATEC Web Conf.
Volume 415, 2025
International Colloquium on Mechanical and Civil Engineering (ICMCE’2025)
Article Number 03001
Number of page(s) 10
Section Artificial Intelligence and Optimization
DOI https://doi.org/10.1051/matecconf/202541503001
Published online 27 October 2025
  1. L. Xu, E. Xu, et L. Li, « Industry 4.0: State of the art and future trends », Int. J. Prod. Res., vol. 56, p. 1‑22, mars 2018, doi: 10.1080/00207543.2018.1444806. [Google Scholar]
  2. Kagermann, H., Wahlster, W. and Helbig, J. (2013) Securing the Future of German Manufacturing Industry Recommendations for Implementing the Strategic Initiative Industrie 4.0. Final Report of the Industrie 4.0 Working Group, Acatech— National Academy of Science and Engineering, 678 p. ­ References ­ Scientific Research Publishing ». Consulté le: 12 mars 2025. [Google Scholar]
  3. Y. Lu, « Industry 4.0: A survey on technologies, applications and open research issues », J. Ind. Inf. Integr., vol. 6, p. 1‑10, juin 2017, doi: 10.1016/j.jii.2017.04.005. [Google Scholar]
  4. Lasi, H., Fettke, P., Kemper, H.G., Feld, T. and Hoffmann, M. (2014) Industry 4.0. Business & Information Systems Engineering, 6, 239-242. ­ References ­ Scientific Research Publishing . Consulté le: 10 mars 2025. [CrossRef] [Google Scholar]
  5. J. Mula, D. Peidro, M. Díaz-Madroñero, et E. Vicens-Salort, « Mathematical programming models for supply chain production and transport planning », Eur. J. Oper. Res., vol. 204, p. 377‑390, août 2010, doi: 10.1016/j.ejor.2009.09.008. [CrossRef] [Google Scholar]
  6. S. Graves, « Manufacturing Planning and Control », janv. 2002. [Google Scholar]
  7. Y. Pochet et L. Wolsey, « Production Planning by Mixed Integer Programming », janv. 2006, doi: 10.1007/0-387-33477-7. [Google Scholar]
  8. F. Hillier et G. Lieberman, « Introduction To Operations Research », in Journal of the Royal Statistical Society. Series A (General), vol. 139, 1969. doi: 10.2307/2345190. [Google Scholar]
  9. R. X. Gao, J. Krüger, M. Merklein, H.-C. Möhring, et J. Váncza, « Artificial Intelligence in manufacturing: State of the art, perspectives, and future directions », CIRP Ann., vol. 73, no 2, p. 723‑749, janv. 2024, doi: 10.1016/j.cirp.2024.04.101. [Google Scholar]
  10. O. Ali, W. Abdelbaki, A. Shrestha, E. Elbasi, M. A. A. Alryalat, et Y. K. Dwivedi, « A systematic literature review of artificial intelligence in the healthcare sector: Benefits, challenges, methodologies, and functionalities », J. Innov. Knowl., vol. 8, no 1, p. 100333, janv. 2023, doi: 10.1016/j.jik.2023.100333. [Google Scholar]
  11. V. Mnih et al., « Human-level control through deep reinforcement learning », Nature, vol. 518, p. 529‑33, févr. 2015, doi: 10.1038/nature14236. [NASA ADS] [CrossRef] [Google Scholar]
  12. S. Hochreiter et J. Schmidhuber, « Long Short-Term Memory », Neural Comput., vol. 9, p. 1735‑1780, nov. 1997, doi: 10.1162/neco.1997.9.8.1735. [CrossRef] [Google Scholar]
  13. M. Ghobakhloo, « The future of manufacturing industry: a strategic roadmap toward Industry 4.0 », J. Manuf. Technol. Manag., vol. 29, juin 2018, doi: 10.1108/JMTM-02-2018-0057. [Google Scholar]
  14. F. Oukhay, P. Zaraté, et T. Romdhane, « Intelligent Decision Support System for Updating Control Plans », 15 juin 2020, arXiv: arXiv:2006.08153. doi: 10.48550/arXiv.2006.08153. [Google Scholar]
  15. S. Singla, « Optimization of Industrial System Using Metaheuristic Algorithms: a Survey », SSRN Electron. J., janv. 2023, doi: 10.2139/ssrn.4559702. [Google Scholar]
  16. A. Bousdekis, K. Lepenioti, D. Apostolou, et G. Mentzas, « A Review of Data-Driven Decision-Making Methods for Industry 4.0 Maintenance Applications », Electronics, vol. 10, p. 828, mars 2021, doi: 10.3390/electronics10070828. [CrossRef] [Google Scholar]
  17. J. P. U. Cadavid, S. Lamouri, B. Grabot, et A. Fortin, « L’Apprentissage Automatique dans la planification et le contr{ô}le de la production : un {é}tat de l’art », 23 juin 2021, arXiv: arXiv:2106.12916. doi: 10.48550/arXiv.2106.12916. [Google Scholar]
  18. B. Wally et al., « Flexible Production Systems: Automated Generation of Operations Plans Based on ISA-95 and PDDL », IEEE Robot. Autom. Lett., vol. 4, no 4, p. 4062‑4069, oct. 2019, doi: 10.1109/LRA.2019.2929991. [Google Scholar]
  19. [M. Schlenkrich, W. Seiringer, K. Altendorfer, et S. N. Parragh, « Enhancing Rolling Horizon Production Planning Through Stochastic Optimization Evaluated by Means of Simulation », 26 septembre 2024, arXiv: arXiv:2402.14506. doi: 10.48550/arXiv.2402.14506. [Google Scholar]
  20. J. Lee, B. Bagheri, et H.-A. Kao, « A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems », Manuf. Lett., vol. 3, p. 18‑23, janv. 2015, doi: 10.1016/j.mfglet.2014.12.001. [Google Scholar]
  21. Russell et S. Norvig, « Artificial Intelligence: A Modern Approach », Prentice Hall Englewood Cliffs NJ, janv. 2010. [Google Scholar]
  22. K. Green, « Conducting Research Literature Reviews: From the Internet to Paper (3rd ed.) By Arlene Fink, SAGE, Los Angeles, CA (2010) 253 pp., $49.95 (pbk), ISBN 978-1412971898 », Libr. Inf. Sci. Res. ­ LIBR Inf. SCI RES, vol. 32, oct. 2010, doi: 10.1016/j.lisr.2010.07.003. [Google Scholar]
  23. S. Liu et H. Cheng, « Manufacturing Process Optimization in the Process Industry », Int. J. Inf. Technol. Web Eng., vol. 19, p. 1‑20, janv. 2024, doi: 10.4018/IJITWE.338998. [Google Scholar]
  24. D. Kiel, J. Müller, C. Arnold, et K.-I. VOIGT, « SUSTAINABLE INDUSTRIAL VALUE CREATION: BENEFITS AND CHALLENGES OF INDUSTRY 4.0 », Int. J. Innov. Manag., vol. 21, p. 1740015, nov. 2017, doi: 10.1142/S1363919617400151. [Google Scholar]
  25. A. Esteso, D. Peidro, J. Mula, et M. Díaz-Madroñero, « Reinforcement learning applied to production planning and control », Int. J. Prod. Res., vol. 61, p. 1‑18, août 2022, doi: 10.1080/00207543.2022.2104180. [Google Scholar]
  26. B. F. Azevedo, A. M. A. C. Rocha, et A. I. Pereira, « Hybrid approaches to optimization and machine learning methods: a systematic literature review », Mach Learn, vol. 113, no 7, p. 4055‑4097, janv. 2024, doi: 10.1007/s10994-023-06467-x. [Google Scholar]
  27. M. Panzer, B. Bender, et N. Gronau, Deep Reinforcement Learning In Production Planning And Control: A Systematic Literature Review. 2021. doi: 10.15488/11238. [Google Scholar]
  28. V. Denhere, Traditional decision making versus artificial intelligence aided decision making in management of firms: A narrative overview. 2021. [Google Scholar]
  29. A. Kusiak, « Smart manufacturing », Int. J. Prod. Res., janv. 2018, Consulté le: 13 janvier 2025. [En ligne]. Disponible sur: https://www.tandfonline.com/doi/abs/10.1080/00207543.2017.1351644 [Google Scholar]
  30. F. Doshi-Velez et B. Kim, « Towards A Rigorous Science of Interpretable Machine Learning », ArXiv Mach. Learn., févr. 2017, Consulté le: 25 mai 2025. [En ligne]. Disponible sur: https://www.semanticscholar.org/paper/Towards-A-Rigorous-Science-of-Interpretable-Machine-Doshi-Velez-Kim/5c39e37022661f81f79e481240ed9b175dec6513 [Google Scholar]
  31. K. Zhou, T. Liu, et L. Zhou, « Industry 4.0: Towards future industrial opportunities and challenges », in 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), août 2015, p. 2147‑2152. doi: 10.1109/FSKD.2015.7382284. [Google Scholar]

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