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Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0

Citations

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Cited by:

  1. Mateo Ramos-Merino & Juan M. Santos-Gago & Luis M. Álvarez-Sabucedo, 2021. "Fuzzy traceability: using domain knowledge information to estimate the followed route of process instances in non-exhaustive monitoring environments," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2235-2255, December.
  2. Behice Meltem Kayhan & Gokalp Yildiz, 2023. "Reinforcement learning applications to machine scheduling problems: a comprehensive literature review," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 905-929, March.
  3. Olumide Emmanuel Oluyisola & Swapnil Bhalla & Fabio Sgarbossa & Jan Ola Strandhagen, 2022. "Designing and developing smart production planning and control systems in the industry 4.0 era: a methodology and case study," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 311-332, January.
  4. Jian Tang & Hao Tian & Tianzheng Wang, 2024. "A Review of Model Predictive Control for the Municipal Solid Waste Incineration Process," Sustainability, MDPI, vol. 16(17), pages 1-35, September.
  5. Anupama Prashar, 2023. "Title: production planning and control in industry 4.0 environment: a morphological analysis of literature and research agenda," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2513-2528, August.
  6. Lemstra, Mary Anny Moraes Silva & de Mesquita, Marco Aurélio, 2023. "Industry 4.0: a tertiary literature review," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
  7. Cruz, Yarens J. & Villalonga, Alberto & Castaño, Fernando & Rivas, Marcelino & Haber, Rodolfo E., 2024. "Automated machine learning methodology for optimizing production processes in small and medium-sized enterprises," Operations Research Perspectives, Elsevier, vol. 12(C).
  8. Tan Ching Ng & Sie Yee Lau & Morteza Ghobakhloo & Masood Fathi & Meng Suan Liang, 2022. "The Application of Industry 4.0 Technological Constituents for Sustainable Manufacturing: A Content-Centric Review," Sustainability, MDPI, vol. 14(7), pages 1-21, April.
  9. SungKu Kang & Ran Jin & Xinwei Deng & Ron S. Kenett, 2023. "Challenges of modeling and analysis in cybermanufacturing: a review from a machine learning and computation perspective," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 415-428, February.
  10. Fangyi Xu & Jihong Wang, 2025. "Harnessing Hybridized Machine Learning Algorithms for Sustainable Smart Production: A Case Study of Solar PV Energy in China," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(1), pages 3214-3264, March.
  11. Rusindiyanto, 2023. "Production Planning and Control of Flooring Using Aggregate Planning Method," Technium, Technium Science, vol. 16(1), pages 397-404.
  12. Mansoureh Maadi & Hadi Akbarzadeh Khorshidi & Uwe Aickelin, 2021. "A Review on Human–AI Interaction in Machine Learning and Insights for Medical Applications," IJERPH, MDPI, vol. 18(4), pages 1-27, February.
  13. Alisha Lakra & Shubhkirti Gupta & Ravi Ranjan & Sushanta Tripathy & Deepak Singhal, 2022. "The Significance of Machine Learning in the Manufacturing Sector: An ISM Approach," Logistics, MDPI, vol. 6(4), pages 1-15, October.
  14. Masoud Zafarzadeh & Magnus Wiktorsson & Jannicke Baalsrud Hauge, 2021. "A Systematic Review on Technologies for Data-Driven Production Logistics: Their Role from a Holistic and Value Creation Perspective," Logistics, MDPI, vol. 5(2), pages 1-32, April.
  15. Sebastian Mayer & Tobias Classen & Christian Endisch, 2021. "Modular production control using deep reinforcement learning: proximal policy optimization," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2335-2351, December.
  16. Nijat Mehdiyev & Maxim Majlatow & Peter Fettke, 2025. "Quantifying and explaining machine learning uncertainty in predictive process monitoring: an operations research perspective," Annals of Operations Research, Springer, vol. 347(2), pages 991-1030, April.
  17. Tan, Daniel & Suvarna, Manu & Shee Tan, Yee & Li, Jie & Wang, Xiaonan, 2021. "A three-step machine learning framework for energy profiling, activity state prediction and production estimation in smart process manufacturing," Applied Energy, Elsevier, vol. 291(C).
  18. Carlos A. Escobar & Megan E. McGovern & Ruben Morales-Menendez, 2021. "Quality 4.0: a review of big data challenges in manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2319-2334, December.
  19. Chenxi Yuan & Guoyan Li & Sagar Kamarthi & Xiaoning Jin & Mohsen Moghaddam, 2022. "Trends in intelligent manufacturing research: a keyword co-occurrence network based review," Journal of Intelligent Manufacturing, Springer, vol. 33(2), pages 425-439, February.
  20. Kyu Tae Park & Jinho Yang & Sang Do Noh, 2021. "VREDI: virtual representation for a digital twin application in a work-center-level asset administration shell," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 501-544, February.
  21. Mario Passalacqua & Robert Pellerin & Florian Magnani & Philippe Doyon-Poulin & Laurène Del-Aguila & Jared Boasen & Pierre-Majorique Léger, 2024. "Human-centred AI in industry 5.0: a systematic review," Post-Print hal-04723054, HAL.
  22. Christian Meske & Enrico Bunde, 2023. "Design Principles for User Interfaces in AI-Based Decision Support Systems: The Case of Explainable Hate Speech Detection," Information Systems Frontiers, Springer, vol. 25(2), pages 743-773, April.
  23. Guilherme Luz Tortorella & Anupama Prashar & Jiju Antony & Flavio S. Fogliatto & Vicente Gonzalez & Moacir Godinho Filho, 2024. "Industry 4.0 adoption for healthcare supply chain performance during COVID-19 pandemic in Brazil and India: the mediating role of resilience abilities development," Operations Management Research, Springer, vol. 17(2), pages 389-405, June.
  24. Shaohua Huang & Yu Guo & Nengjun Yang & Shanshan Zha & Daoyuan Liu & Weiguang Fang, 2021. "A weighted fuzzy C-means clustering method with density peak for anomaly detection in IoT-enabled manufacturing process," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1845-1861, October.
  25. Guo, Daqiang & Li, Mingxing & Lyu, Zhongyuan & Kang, Kai & Wu, Wei & Zhong, Ray Y. & Huang, George Q., 2021. "Synchroperation in industry 4.0 manufacturing," International Journal of Production Economics, Elsevier, vol. 238(C).
  26. Xiaohan Li & Chenwei Ma & Yang Lv, 2022. "Environmental Cost Control of Manufacturing Enterprises via Machine Learning under Data Warehouse," Sustainability, MDPI, vol. 14(18), pages 1-21, September.
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