Machine Learning Applications for Production Scheduling Optimization
DOI:
https://doi.org/10.31004/edp.v2i4.137Keywords:
production scheduling, machine learning, interoperability, optimizationAbstract
Production scheduling represents a critical function within manufacturing and industrial operations, exerting a direct influence on productivity, operational efficiency, and overall cost management. Traditional scheduling methodologies, while foundational, often exhibit limitations when confronted with the complexity, variability, and dynamic demands of contemporary production environments. In response, this paper investigates the potential of Machine Learning (ML) techniques for the enhancement of production scheduling outcomes. Specifically, it examines the capabilities of reinforcement learning, neural networks, and genetic algorithms to model complex systems, adapt to real-time disruptions, and support more effective decision-making processes. The paper further reviews notable industrial applications of these techniques, critically evaluating their performance relative to conventional methods. In addition, it addresses the inherent challenges associated with the deployment of ML in production scheduling, including data availability, algorithmic interpretability, and integration with legacy systems. Finally, the study outlines future research directions, emphasizing the need for more robust, scalable, and interpretable ML-based scheduling solutions to meet the evolving demands of modern industry.
References
Conrad, F., Boos, E., Mälzer, M., Wiemer, H., and Ihlenfeldt, S. (2023). Impact of Data Sampling on Performance and Robustness of Machine Learning Models in Production Engineering. In Lecture Notes in Production Engineering (pp. 463–472). https://doi.org/10.1007/978-3-031-18318-8_47
Esteso, A., Peidro, D., Mula, J., and Díaz-Madroñero, M. (2022). Reinforcement Learning Applied to Production Planning and Control. International Journal of Production Research, 61(16), 5772–5789. https://doi.org/10.1080/00207543.2022.2104180
Gatla, N. T. R., and Mandati, N. S. R. (2020). Optimizing Supply Chain Efficiency through Machine Learning-Driven Predictive Analytics. International Journal of Innovations in Engineering Research and Technology (IJIERT), 7(3), 67–80. https://doi.org/10.26662/ijiert.v7i3.pp 67- 80
Hasan, S. K., Islam, M. A., Asha, A. I., Priya, S. A., and Islam, N. M. (2024). The Integration of AI and Machine Learning in Supply Chain Optimization: Enhancing Efficiency and Reducing Costs. International Journal for Multidisciplinary Research, 6(5). https://doi.org/10.36948/ijfmr.2024.v06i05.28075
Hemanth, G., Eswar, G., Janardhana, S., and Chandrakanth, M. (2024). Data Engineering Solutions: The impact of AI and ML on ERP Systems and Supply Chain Management. Nanotechnology Perceptions, 20 (S9). https://doi.org/10.62441/nano-ntp.v20is9.47
Huang, X. (2018). Solving the Problem of Multi-Objective Flexible Job Shop Based on Hybrid Genetic Algorithm and Particle Swarm Optimization. Proceedings of the 2018 International Conference on Mathematics, Modelling, Simulation and Algorithms (MMSA 2018). https://doi.org/10.2991/mmsa-18.2018.42
Igbokwe, N. C., Okpala, C. C. and Nwamekwe, C. O. (2024). The Implementation of Internet of Things in the Manufacturing Industry: An Appraisal. International Journal of Engineering Research and Development, vol. 20, iss. 7, https://www.ijerd.com/paper/vol20-issue7/2007510516.pdf
Li, H., Zhang, H., He, Z., Jia, Y., Jiang, B., Huang, X., and Ge, D. (2024). Solving Integrated Process Planning and Scheduling Problem via Graph Neural Network Based Deep Reinforcement Learning. arxiv.org/pdf/2409.00968 (Cornell University). https://doi.org/10.48550/arXiv.2409.00968
Mallreddy, N. S. R. (2022). Optimizing CI/CD Workflows with Machine Learning: Predictive Resource Allocation for Enhanced Deployment Efficiency. IJRDO -Journal of Computer Science Engineering, 8(7), 31–37. https://doi.org/10.53555/cse.v8i7.6130
Mgbemena, C. E., Onuoha D. O., Okpala C. C. and Mgbemena C. O. (2020). Design and Development of a Proximity Warning System for Improved Safety on the Manufacturing Shop Floor. Elsevier-Journal of King Saud University – Engineering Sciences, https://authors.elsevier.com/sd/article/S1018-3639(20)30341-X
Muccini, H. and Vaidhyanathan, K. (2021). Software Architecture for ML-Based Systems: What Exists and What Lies Ahead. Conference: AI Engineering - Software Engineering for AI (WAIN) Workshop at ICSE 2021At: Madrid, Spain, 121–128. https://doi.org/10.1109/wain52551.2021.00026
Nagesh, N. C., Gowd, N. G. S., Kumar, N. N. K., and Reddy, N. G. P. (2024). Machine Learning Techniques to Optimize CPU Scheduling in Real-Time Systems: A Comprehensive Review and Analysis. International Journal of Advanced Research in Science Communication and Technology, 381–388. https://doi.org/10.48175/ijarsct-18941
Nwamekwe, C. O. and Okpala, C. C. (2025). Machine Learning-Augmented Digital Twin Systems for Predictive Maintenance in High-Speed Rail Networks. International Journal of Multidisciplinary Research and Growth Evaluation, vol. 6, iss. 1, https://www.allmultidisciplinaryjournal.com/uploads/archives/ 20250212104201_MGE-2025-1-306.1.pdf
Nwamekwe, C. O., Okpala, C. C. and Okpala, S. C. (2024). Machine Learning-Based Prediction Algorithms for the Mitigation of Maternal and Fetal Mortality in the Nigerian Tertiary Hospitals. International Journal of Engineering Inventions, vol. 13, iss. 7, http://www.ijeijournal.com/papers/Vol13-Issue7/1307132138.pdf
Nwamekwe, C. O., Ewuzie, N. V. Okpala, C. C., Ezeanyim, O. C., Nwabueze, C. V. and Nwabunwanne, E. C. (2025). Optimizing Machine Learning Models for Soil Fertility Analysis: Insights from Feature Engineering and Data Localization. Gazi University Journal of Science, vol. 12, iss. 1, https://dergipark.org.tr/en/pub/gujsa/issue/90827/1605587
Nwankwo, C. O., Okpala, C. C. and Igbokwe, N. C. (2024). Enhancing Smart Manufacturing Supply Chains Through Cybersecurity Measures. International Journal of Engineering Inventions, vol. 13, iss. 12, https://www.ijeijournal.com/papers/Vol13-Issue12/13120106.pdf
Okpala, C. C. and Udu, C. E. (2025a). Big Data Applications in Manufacturing Process Optimization. International Journal of Multidisciplinary Research and Growth Evaluation, vol. 6, iss. 1, https://www.allmultidisciplinaryjournal.com/uploads/archives/20250212105349_MGE-2025-1-308.1.pdf
Okpala, C. C. and Udu, C. E. (2025b). Artificial Intelligence Applications for Customized Products Design in Manufacturing. International Journal of Multidisciplinary Research and Growth Evaluation, vol. 6, iss. 1, https://www.allmultidisciplinaryjournal.com/uploads/archives/20250212104938_MGE-2025-1-307.1.pdf
Okpala, C. C., Udu, C. E. and Okpala, S. C. (2025a). Big Data and Artificial Intelligence Implementation for Sustainable HSE Practices in FMCG. International Journal of Engineering Inventions, vol. 14, iss. 5, file:///C:/Users/Admin/Downloads/14050107-1.pdf
Okpala, C. C., Udu, C. E. and Nwamekwe, C. O. (2025b). Artificial Intelligence-Driven Total Productive Maintenance: The Future of Maintenance in Smart Factories. International Journal of Engineering Research and Development, vol. 21, iss. 1, https://ijerd.com/paper/vol21-issue1/21016874.pdf
Okpala, C. C., Udu, C. E. and Nwankwo, C. O. (2025c). Digital Twin Applications for Predicting and Controlling Vibrations in Manufacturing Systems. World Journal of Advanced Research and Reviews, vol. 25, iss. 01, https://doi.org/10.30574/wjarr.2025.25.1.3821
Polyzotis, N., Roy, S., Whang, S. E., and Zinkevich, M. (2017). Data Management Challenges in Production Machine Learning. Conference: The 2017 ACM International Conference. https://doi.org/10.1145/3035918.3054782
Priore, P., Gómez, A., Pino, R., and Rosillo, R. (2014). Dynamic Scheduling of Manufacturing Systems Using Machine Learning: An Updated Review. Artificial Intelligence for Engineering Design Analysis and Manufacturing, 28(1), 83–97. https://doi.org/10.1017/s0890060413000516
Rajawat, A. S., Goyal, S. B., Kumar, M., and Malik, V. (2024). Adaptive Resource Allocation and Optimization in Cloud Environments: Leveraging Machine Learning for Efficient Computing. In CRC Press eBooks (pp. 499 – 508). https://doi.org/10.1201/9781003471059-64
Rane, N. L., Desai, P., Rane, J., and Paramesha, M. (2024). Artificial Intelligence, Machine Learning, and Deep Learning for Sustainable and Resilient Supply Chain and Logistics Management. In Trustworthy Artificial Intelligence in Industry and Society. https://doi.org/10.70593/978-81-981367-4-9_5
Sandhu, M., Saini, A., Kaur, G., and Purnima, N. (2024). Reinforcement Learning: Framework, Applications and Challenges. International Journal of Engineering Science and Humanities. 14 (Special Issue 1), 58–64. https://doi.org/10.62904/s3qf5660
Singh, A. (2024). Engineering Robust Machine Learning Systems: A Framework for Production-Grade Deployments. International Journal of Research in Computer Applications and Information Technology (IJRCAIT), 7(2), 2731–2741. https://doi.org/10.34218/ijrcait_07_02_208
Sun, D., and Zang, H. (2024). Research on the Application of Evolutionary Algorithms Enhanced by Deep Learning in Intelligent Manufacturing Scheduling. 2024 5th International Conference on Machine Learning and Computer Application (ICMLCA), Hangzhou, China, 518–525. https://doi.org/10.1109/icmlca63499.2024.10754379
Takeda-Berger, S. L., Frazzon, E. M., Broda, E., and Freitag, M. (2020). Machine Learning in Production Scheduling: An Overview of the Academic literature. In Lecture notes in logistics (pp. 409–419). https://doi.org/10.1007/978-3-030-44783-0_39
Udu, C. E., Ejichukwu, E. O. and Okpala, C. C. (2025). The Application of Digital Tools for Supply Chain Optimization. International Journal of Multidisciplinary Research and Growth Evaluation, vol. 6, iss. 3. https://www.allmultidisciplinaryjournal.com/uploads/archives/20250508172828_MGE-2025-3-047.1.pdf
Udu, C. E. and Okpala, C. C. (2025). Digital Twin Technology in Water Treatment: Real-Time Process Optimization and Environmental Impact Reduction. International Journal of Engineering Inventions, vol. 14, iss. 5, pp 8 – 15. file:///C:/Users/Admin/Downloads/14050815.pdf
Yang, R., Ouyang, X., Chen, Y., Townend, P., and Xu, J. (2018). Intelligent Resource Scheduling at Scale: A Machine Learning perspective. Conference: IEEE International Symposium on Service Oriented System Engineering, 132–141. https://doi.org/10.1109/sose.2018.00025
Zhang, L., Liu, Z., Zhou, Y., Wu, T., and Sun, J. (2024). Grounding Large Language Models in Real-World Environments Using Imperfect World Models. Authorea. https://doi.org/10.22541/au.172832793.37033556/v1
Zhao, H., Chen, Y., Dang, B., and Jian, X. (2024). Research on Steel Production Scheduling Optimization Based on Deep Learning. Preprint. https://doi.org/10.20944/preprints202412.1502.v1
Zhao, L., Hou, X., and Luo, Z. (2023). Modeling and Optimization of Distributed Scheduling for Intelligent Manufacturing Based on Machine Learning. Conference: 2023 International Conference on Mechatronics, IoT and Industrial Informatics (ICMIII), 631–634. https://doi.org/10.1109/icmiii58949.2023.00132
Zhao, L., Shen, W., Zhang, C., and Peng, K. (2022). An End-to-End Deep Reinforcement Learning Approach for Job Shop Scheduling. 2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD), 841–846. https://doi.org/10.1109/cscwd54268.2022.9776116
Zhao, Y., and Zhang, H. (2021). Application of Machine Learning and Rule Scheduling in a Job-Shop Production Control System. International Journal of Simulation Modeling, 20(2), 410 – 421. https://doi.org/10.2507/ijsimm20-2-co10
Zhu, H., Gui, Y., Xu, H., Tao, S., and Zheng, K. (2022). Research on a Real-Time Job-Shop Scheduling Method Based on Reinforcement Learning. Journal of Physics Conference Series, 2402(1), 012016. https://doi.org/10.1088/1742-6596/2402/1/012016
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