Artificial Intelligence Enhanced Human–Machine Job Scheduling in a Hybrid Printing System: A Review

The review examined the manufacturing context, scheduling challenges, role of artificial intelligence (AI), review methodology, key findings, and research implications. Hybrid printing integrates traditional, digital, and additive manufacturing technologies to enhance flexibility, customization, material efficiency, and production capacity. However, traditional rule-based and static optimization approaches are unable to effectively handle the complex scheduling challenges created by the coexistence of heterogeneous machines, diverse materials, dynamic production requirements, maintenance constraints, and human involvement. Thus, the review looks at how artificial intelligence (AI) methods such as machine learning, deep learning, reinforcement learning, multi-agent systems, swarm intelligence, and explainable AI (XAI) are applied to intelligent scheduling in hybrid printing environments. In the context of the Industry 5.0 paradigm, it further explores human-in-the-loop methods that integrate computational optimization with human knowledge, contextual judgment, and operational oversight. In order to assess scheduling objectives, algorithms, performance metrics, technological architectures, implementation difficulties, and new research directions, the review synthesizes recent academic literature. Results show that makespan, resource utilization, throughput, adaptability, energy efficiency, and responsiveness to production disruptions can all be improved with AI-enhanced scheduling. Real-time decision-making, transparency, and operator trust are further improved by integration with digital twins, XAI, and the Industrial Internet of Things (IIoT). The review comes to the conclusion that collaborative scheduling between humans and AI offers a promising basis for creating intelligent, robust, and sustainable hybrid printing systems.

Artificial Intelligence; Human–Machine Collaboration; Job Scheduling; Hybrid Printing Systems; Industry 5.0

1. Altun, F., Bayar, A., Hamzat, A. K., Asmatulu, R., Ali, Z., & Asmatulu, E. (2025). AI-Driven innovations in 3D printing: optimization, automation, and intelligent control. Journal of Manufacturing and Materials Processing, 9(10), 329. https://doi.org/10.3390/jmmp9100329 [Google Scholar] [Crossref]

2. Anang, A. N., Obidi, P. O., Mesogboriwon, A. O., Obidi, J. O., Kuubata, M., & Ogunbiyi, D. (2024). THE role of Artificial Intelligence in industry 5.0: Enhancing human-machine collaboration. World Journal of Advanced Research and Reviews, 24(2), 380–400. [Google Scholar] [Crossref]

3. https://doi.org/10.30574/wjarr.2024.24.2.3369 [Google Scholar] [Crossref]

4. Aremu, V. I., & Udofia, I. G. (2025). Impact of digital literacy skills on undergraduate performance in Nigeria. African Journal of Applied Research, 11(2), 210–219. [Google Scholar] [Crossref]

5. https://doi.org/10.26437/ajar.v11i2.1031 [Google Scholar] [Crossref]

6. Attaran, S., Attaran, M., & Celik, B. G. (2024). Digital Twins and Industrial Internet of Things: Uncovering operational intelligence in industry 4.0. Decision Analytics Journal, 10, 100398. https://doi.org/10.1016/j.dajour.2024.100398 [Google Scholar] [Crossref]

7. Barua, D. A., Sami, S. A., & Barua, L. (2025). Leveraging artificial intelligence for smart production management in industry 4.0. Scientific Reports, 15(1), 41559. https://doi.org/10.1038/s41598-025-25413-6 [Google Scholar] [Crossref]

8. Carreno, A. M. (2024). Building a continuous Feedback Loop for Real-Time Change adaptation: best practices and tools. Zenodo (CERN European Organization for Nuclear Research). https://doi.org/10.5281/zenodo.14051466 [Google Scholar] [Crossref]

9. Chen, J., & Zhou, X. (2025). Reinforcement learning based maintenance scheduling of flexible multi-machine manufacturing systems with varying interactive degradation. Reliability Engineering & System Safety, 260, 111018. https://doi.org/10.1016/j.ress.2025.111018 [Google Scholar] [Crossref]

10. Chikwendu, O. C., & Emeka, U. C. (2025). Recent innovations in additive manufacturing for industrial applications. International Journal of Latest Technology in Engineering Management & Applied Science, 14(3), 164–178. https://doi.org/10.51583/ijltemas.2025.140300021 [Google Scholar] [Crossref]

11. Chikwendu, O. C., Emeka, U. C., & Obiuto, N. C. (2025). Digital twin applications for predicting and controlling vibrations in manufacturing systems. World Journal of Advanced Research and Reviews, 25(1), 764–772. https://doi.org/10.30574/wjarr.2025.25.1.3821 [Google Scholar] [Crossref]

12. Dhanda, M., Rogers, B. A., Hall, S., Dekoninck, E., & Dhokia, V. (2024). Reviewing human-robot collaboration in manufacturing: Opportunities and challenges in the context of industry 5.0. Robotics and Computer-Integrated Manufacturing, 93, 102937. https://doi.org/10.1016/j.rcim.2024.102937 [Google Scholar] [Crossref]

13. Hedea, I. (2025). Algorithms for dynamic scheduling in manufacturing, towards digital factories Improving Deadline Feasibility and Responsiveness via Temporal Networks. Zenodo (CERN European Organization for Nuclear Research). https://doi.org/10.13140/rg.2.2.28196.13448 [Google Scholar] [Crossref]

14. Helal, W. M. K. (2025). 3D printing and additive manufacturing: technologies, applications, and future directions. https://doi.org/10.70593/978-93-7185-095-7 [Google Scholar] [Crossref]

15. Jin, J. (2025). Enhancing Manufacturing Performance with AI and Machine Learning: Applications in Predictive Maintenance and Production Optimization. Applied and Computational Engineering, 140(1), 85–90. https://doi.org/10.54254/2755-2721/2025.21393 [Google Scholar] [Crossref]

16. Jin, W., Wang, N., Zhang, L., Tian, X., Shi, B., & Zhao, B. (2025). A review of AI-Driven Automation Technologies: latest taxonomies, existing challenges, and future prospects. Computers, Materials & Continua/Computers, Materials & Continua (Print), 84(3), 3961–4018. [Google Scholar] [Crossref]

17. https://doi.org/10.32604/cmc.2025.067857 [Google Scholar] [Crossref]

18. Joshi, M. (2023). Adaptive learning through artificial intelligence. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4514887 [Google Scholar] [Crossref]

19. Joshi, M. A. (2026). AI for Supply chain & logistics: Transforming operations through intelligent automation, predictive analytics, and decision intelligence. Preprint. [Google Scholar] [Crossref]

20. https://doi.org/10.13140/rg.2.2.19616.75524 [Google Scholar] [Crossref]

21. Khurram, M., Zhang, C., Muhammad, S., Kishnani, H., An, K., Abeywardena, K., Chadha, U., & Behdinan, K. (2025). Artificial intelligence in manufacturing industry Worker Safety: A new paradigm for hazard Prevention and Mitigation. Processes, 13(5), 1312. https://doi.org/10.3390/pr13051312 [Google Scholar] [Crossref]

22. Lamdjad, B., & Chaiter, A. (2025). An explainable Human–AI Co-Decision framework for Real-Time scheduling in digital Twin–Enabled smart factories. SSRN Electronic Journal. [Google Scholar] [Crossref]

23. https://doi.org/10.2139/ssrn.5910569 [Google Scholar] [Crossref]

24. Mehta, R. (2025). Strategic integration of ERP and manufacturing information Systems: Overcoming implementation challenges and driving digital transformation. Journal of Information Systems Engineering & Management, 10(45s), 835–845. https://doi.org/10.52783/jisem.v10i45s.9034 [Google Scholar] [Crossref]

25. Nguyen, P., Kim, M., Nichols, E., & Yoon, H. (2025). AI-Driven Digital Twins for Manufacturing: A Review Across Hierarchical Manufacturing System Levels. Sensors, 26(1), 124. [Google Scholar] [Crossref]

26. https://doi.org/10.3390/s26010124 [Google Scholar] [Crossref]

27. Nikiforidis, K., Kyrtsoglou, A., Vafeiadis, T., Kotsiopoulos, T., Nizamis, A., Ioannidis, D., Votis, K., Tzovaras, D., & Sarigiannidis, P. (2024). Enhancing transparency and trust in AI-powered manufacturing: A survey of explainable AI (XAI) applications in smart manufacturing in the era of industry 4.0/5.0. ICT Express, 11(1), 135–148. https://doi.org/10.1016/j.icte.2024.12.001 [Google Scholar] [Crossref]

28. Rajkumar, N., Nachiappan, B., Mathews, A., Radha, V., Viji, C., & Kovilpillai, J. A. (2024). Industry 5.0: The Human-centric future of manufacturing. In Challenges in Information, Communication and Computing Technology (pp. 562–567). https://doi.org/10.1201/9781003559085-97 [Google Scholar] [Crossref]

29. Rane, N. L., Kaya, Ö., & Rane, J. (2024). Human-centric Artificial Intelligence in Industry 5.0: Enhancing human interaction and collaborative applications. In Artificial Intelligence, Machine Learning, and Deep Learning for Sustainable Industry 5.0. https://doi.org/10.70593/978-81-981271-8-1_5 [Google Scholar] [Crossref]

30. Rojek, I., Mikołajewski, D., Dostatni, E., Cybulski, J., & Kozielski, M. (2025). Personalization of AI-Based Digital Twins to Optimize adaptation in Industrial Design and Manufacturing Review. Applied Sciences, 15(15), 8525. https://doi.org/10.3390/app15158525 [Google Scholar] [Crossref]

31. Said, L. B., Ayadi, B., Alharbi, S., & Dammak, F. (2025). Recent Advances in Additive Manufacturing: A review of current developments and future directions. Machines, 13(9), 813. [Google Scholar] [Crossref]

32. https://doi.org/10.3390/machines13090813 [Google Scholar] [Crossref]

33. Schneck, M., Horn, M., Schmitt, M., Seidel, C., Schlick, G., & Reinhart, G. (2021). Review on additive hybrid- and multi-material-manufacturing of metals by powder bed fusion: state of technology and development potential. Progress in Additive Manufacturing, 6(4), 881–894. [Google Scholar] [Crossref]

34. https://doi.org/10.1007/s40964-021-00205-2 [Google Scholar] [Crossref]

35. Tolia, A., & Ponis, S. T. (2026). Digital Twins for Real-Time Decision-Making in Supply Chain Management and Logistics: A Systematic Review. Information, 17(8), 732. [Google Scholar] [Crossref]

36. https://doi.org/10.3390/info17080732 [Google Scholar] [Crossref]

37. Trivedi, C., Bhattacharya, P., Prasad, V. K., Patel, V., Singh, A., Tanwar, S., Sharma, R., Aluvala, S., Pau, G., & Sharma, G. (2024). Explainable AI for Industry 5.0: Vision, architecture, and potential Directions. IEEE Open Journal of Industry Applications, 5, 177–208. [Google Scholar] [Crossref]

38. https://doi.org/10.1109/ojia.2024.3399057 [Google Scholar] [Crossref]

39. Udu, C.E., Okpala, C.C., & Nwamekwe, C.O (2025). Human-Centric Design Integration in Industry 5.0: A Framework for Resilient Smart Manufacturing. (2025). International Journal of Industrial and Production Engineering, 3(4), 18- 33. https://journals.unizik.edu.ng/ijipe/article/view/6772, https://www.researchgate.net/publication/398689252_Human-Centric_Design_Integration_in_Industry_50_A_Framework_for_Resilient_Smart_Manufacturing [Google Scholar] [Crossref]

40. Wang, T., & Zhan, W. (2024). Design and control a hybrid human–machine collaborative manufacturing system in operational management technology to enhance human–machine collaboration. The International Journal of Advanced Manufacturing Technology. [Google Scholar] [Crossref]

41. https://doi.org/10.1007/s00170-024-14894-w [Google Scholar] [Crossref]

42. Wu, L., Sha, K., Tao, Y., Ju, B., & Chen, Y. (2023). A hybrid deep learning model as the digital twin of Ultra-Precision Diamond Cutting for In-Process prediction of Cutting-Tool Wear. Applied Sciences, 13(11), 6675. https://doi.org/10.3390/app13116675 [Google Scholar] [Crossref]

43. Xia, B., Wu, K., Zhang, Q., Peng, Y., & Gao, Y. (2026). Energy-Aware scheduling for sustainable manufacturing: integrating production systems and HVAC control. Sustainability, 18(12), 6219. https://doi.org/10.3390/su18126219 [Google Scholar] [Crossref]

44. Yahya, L. M., Suharni, S., Hidayat, D., & Vandika, A. Y. (2024). Application of artificial intelligence to improve production process efficiency in manufacturing industry. West Science Information System and Technology, 2(02), 223–232. https://doi.org/10.58812/wsist.v2i02.1221 [Google Scholar] [Crossref]

45. Zhang, Y., Xiao, W., & Bi, Y. (2025). Integrated Predictive-Maintenance and MPC Scheduling: Achieving high availability in smart manufacturing. IEEE Access, 13, 177694–177705. [Google Scholar] [Crossref]

46. https://doi.org/10.1109/access.2025.3619999 [Google Scholar] [Crossref]

47. Zhou, L., Miller, J., Vezza, J., Mayster, M., Raffay, M., Justice, Q., Tamimi, Z. A., Hansotte, G., Sunkara, L. D., & Bernat, J. (2024). Additive Manufacturing: A Comprehensive review. Sensors, 24(9), 2668. https://doi.org/10.3390/s24092668 [Google Scholar] [Crossref]

48. Zhou, W., Ding, X., Xie, Z., Sun, M., & Tan, Z. (2024). Integrated scheduling algorithm with Dynamic Adjustment on Machine Idle Time. Research Square. https://doi.org/10.21203/rs.3.rs-4302637/v1 [Google Scholar] [Crossref]

49. Zhu, X., Liao, B., Shen, Y., & Kong, M. (2025). Towards Industry 5.0: digital twin-enhanced approach for dynamic supply chain rescheduling with real-time order arrival and acceptance. International Journal of Production Research, 64(13), 5588–5610. https://doi.org/10.1080/00207543.2025.2481184 [Google Scholar] [Crossref]

• Impact of AI Tools on Student Learning
• Repogent: An Autonomous Multi-Agent System for End-To-End Repository Maintenance
• Trust-Centric Federated Learning Using Blockchain-Enabled Smart Contracts: An Analysis of Data Privacy, Model Integrity, and Decentralized AI Governance
• Can AI Care? Rethinking Ethics, Clinical Integrity, and Professional Responsibility in the Digital Age of Mental Healthcare
• AI-Based Smart Irrigation System Using IoT and Machine Learning for Precision Agriculture