Order Acceptance and Scheduling in Manufacturing Systems: A Reinforcement Learning Approach

wseas-12082-20260923100007-f0bdd4 20260923100007043 wseas/wseas wseas.group@gmail.com WSEAS WSEAS Transactions on Circuits and Systems 1109-2734 2224-266X 03 30 2026 03 30 2026 25 Order Acceptance and Scheduling in Manufacturing Systems: A Reinforcement Learning Approach Daschievici Luiza Faculty of Engineering and Agronomy, Braila “Dunarea de Jos” University of Galati 47, Domneasca St., Galati ROMANIA This research introduces Smart-OAS (Order Acceptance and Scheduling), a Reinforcement Learning-based methodological framework for the integrated OAS process. The research addresses the limitations of traditional heuristic rules, whose efficiency decreases significantly in the presence of critical bottleneck resources. The system uses an intelligent agent structured on the basis of Markov Decision Processes (MDP) for the dynamic shop floor status assessment, facilitating the integration of data flows from ERP (Enterprise Resource Planning) and MES (Manufacturing Execution Systems) systems. The novelty lies in the design of a reward function that balances marginal profit with opportunity cost and delay-related penalties. Conceptual validation attests the algorithm’s ability to develop superior decision-making policies by strategically prioritizing high-value-added orders and protecting production capacity on critical machines. The study establishes a roadmap for integrating the model into Industry 4.0 ecosystems, exploring the potential for expansion towards digital twin technologies and sustainability indicators. 09 23 2026 09 23 2026 324 29 https://creativecommons.org/licenses/by/4.0/ 10.37394/23201.2026.25.29 https://wseas.com/journals/articles.php?id=12082 Lasi, H., Fettke, P., Kemper, H. G., Feld, T., & Hoffmann, M., Industry 4.0. Business & Information Systems Engineering, 6(4), 2014, 239-242, https://doi.org/10.1007/s12599-014-0334-4 Slotnick, S. A., Order acceptance and scheduling: A taxonomy and review, European Journal of Operational Research, Elsevier, 212(1), 2011, 1-11, DOI 10.1016/j.ejor.2010.09.042 Framinan, J. M., & Ruiz, R., Architecture of manufacturing scheduling systems: Literature review and an integrated proposal, European Journal of Operational Research, Elsevier, 205(2), 2010, 237-246, doi: 10.1016/j.ejor.2009.09.026 Sutton, R. S., & Barto, A. G., Reinforcement Learning: An Introduction. The MIT Press Cambridge, Massachusetts, (2nd Edition), ISBN 978-0262039246, 2018. Wang, J., Xu, C., Zhang, J., & Zhong, R. Y., Big data analytics for intelligent manufacturing systems: A review, Journal of Manufacturing Systems, 62, 2022, 738-752, DOI 10.1016/j.jmsy.2021.03.005 Cesaret, B., Oguz, C., & Salman, F. S., A tabu search algorithm for order acceptance and scheduling, Computers & Operations Research, 39(6), 2012, 1197-1205, doi: 10.1016/j.cor.2010.09.018 Pinedo, M. L., Scheduling: Theory, Algorithms, and Systems, Fourth Edition, Publisher: Springer, ISBN 978-1-4614-1986-0, 2010. Lingling Lv, Chunjiang Z, Jiaxin F, Weiming S, Deep reinforcement learning for job shop scheduling problems: A comprehensive literature review, Knowledge-Based Systems, (321), 2025, Page 113633, doi: 10.1016/j.knosys.2025.113633 Luo, S., Dynamic scheduling for flexible job shop with new job insertions by deep reinforcement learning, Applied Soft Computing, vol.91, 2020, pg.106208, https://doi.org/10.1016/j.asoc.2020.106208 Trentesaux, D., Distributed control of production systems, Engineering Applications of Artificial Intelligence, Elsevier, 22(7), 2009, 971-978, doi: 10.1016/j.engappai.2009.05.001 L.Zhou, L. Zhang, B. K.P. Horn, Deep reinforcement learning-based dynamic scheduling in smart manufacturing, Procedia CIRP, Volume 93, 2020, 383-388, doi: 10.1016/j.procir.2020.05.163 Zheng, C., Yu, J., & Wan, G., Online order acceptance and scheduling in a single machine environment, Computers & Operations Research, Volume 179, July 2025, pg. 107028, https://doi.org/10.1016/j.cor.2025.107028 Mourtzis, D., Simulation in the design and operation of manufacturing systems: state of the art and new trends, International Journal of Production Research, 58(7), 2019, 1927-1949, doi: 10.1080/00207543.2019.1636321. L. Daschievici, D. Ghelase, Order earning power assessment of make-to-order manufacturing system, ModTech International Conference–New face of TMCR Proceedings, Iasi, Romania, 2012, pg. 265-268, ISSN 2069-6736.