Optimization of Production Scheduling Using Machine Learning: A Systematic Literature Review
DOI:
https://doi.org/10.31004/jutin.v9i1.51927Keywords:
Job Shop Scheduling, Artificial Intelligence, Systematic Literatur ReviewAbstract
Modern production systems are increasingly complex, requiring scheduling methods that can handle dynamic environments, diverse constraints, and large-scale operations. Traditional approaches often lack flexibility, while machine learning (ML)–based methods, despite their potential, still face limitations related to scalability, generalizability, interpretability, and computational efficiency. This study presents a systematic literature review of 77 primary studies published between 2014 and 2024, conducted in accordance with the Kitchenham and Charters framework. The review analyzes major research outlets, commonly applied ML techniques, reported performance, and proposed enhancements. Reinforcement learning, particularly deep reinforcement learning, dominates the literature, with methods such as Q-Learning, Deep Q-Networks, and Proximal Policy Optimization showing promise for dynamic scheduling. However, challenges remain regarding convergence speed, data requirements, reward design, and real-time adaptability. Future research should focus on scalable, adaptive, interpretable models and tighter integration with real-time data and Industry 4.0 systems.References
Alexopoulos, K., Nikolakis, N., Bakopoulos, E., Siatras, V., & Mavrothalassitis, P. (2023). Machine Learning Agents Augmented by Digital Twinning for Smart Production Scheduling. 22nd IFAC World Congress, 56(2), 2963–2968. https://doi.org/10.1016/j.ifacol.2023.10.1420
Alicastro, M., Ferone, D., Festa, P., Fugaro, S., & Pastore, T. (2021). A reinforcement learning iterated local search for makespan minimization in additive manufacturing machine scheduling problems. Computers and Operations Research, 131. Scopus. https://doi.org/10.1016/j.cor.2021.105272
Benda, F., Braune, R., Doerner, K. F., & Hartl, R. F. (2019). A machine learning approach for flow shop scheduling problems with alternative resources, sequence-dependent setup times, and blocking. OR Spectrum, 41(4), 871–893. Scopus. https://doi.org/10.1007/s00291-019-00567-8
Campos, T. P. D., Damasceno, E. F., & Valentim, N. M. C. (2022). Porifera: A Collaborative Tool to Support Systematic Literature Review and Systematic Mapping Study. Proceedings of the XXXVI Brazilian Symposium on Software Engineering, 452–457. https://doi.org/10.1145/3555228.3555273
De, G., Sondhi, N., Bhattacharjee, A., & Joshi, H. (2024). Preventive healthcare behavior: A hybrid systematic literature review (1998–2023). International Journal of Consumer Studies, 48(1), e13000. https://doi.org/10.1111/ijcs.13000
Febriani, A. W., Soetjipto, B. E., & Churiyah, M. (2023). Systematic Literature Review Dan Analisis Bibliometrik Pengaruh Work From Home (WFH) Terhadap Produktivitas Kerja Karyawan. Ganaya : Jurnal Ilmu Sosial Dan Humaniora, 6(3), 539–556. https://doi.org/10.37329/ganaya.v6i3.2402
Frye, M., Gyulai, D., Bergmann, J., & Schmitt, R. H. (2020). Production rescheduling through product quality prediction. 54, 142–147. Scopus. https://doi.org/10.1016/j.promfg.2021.07.022
Fülöp, M. T., Gubán, M., Gubán, Á., & Avornicului, M. S. (2022). Application Research of Soft Computing Based on Machine Learning Production Scheduling. Processes. https://api.semanticscholar.org/CorpusID:247301044
Geurtsen, M., Adan, I., & Atan, Z. (2023). Deep reinforcement learning for optimal planning of assembly line maintenance. Journal of Manufacturing Systems, 69, 170–188. Scopus. https://doi.org/10.1016/j.jmsy.2023.05.011
Ghasemi, A., Kabak, K. E., & Heavey, C. (2022). Demonstration of the Feasibility of Real Time Application of Machine Learning to Production Scheduling. 2022-December, 3406–3417. Scopus. https://doi.org/10.1109/WSC57314.2022.10015436
Gil, C.-B., & Lee, J.-H. (2022). Deep Reinforcement Learning Approach for Material Scheduling Considering High-Dimensional Environment of Hybrid Flow-Shop Problem. Applied Sciences (Switzerland), 12(18). Scopus. https://doi.org/10.3390/app12189332
Grumbach, F., Badr, N. E. A., Reusch, P., & Trojahn, S. (2023). A Memetic Algorithm With Reinforcement Learning for Sociotechnical Production Scheduling. IEEE Access, 11, 68760–68775. Scopus. https://doi.org/10.1109/ACCESS.2023.3292548
Grumbach, F., Müller, A., Reusch, P., & Trojahn, S. (2022). Robust-stable scheduling in dynamic flow shops based on deep reinforcement learning. Journal of Intelligent Manufacturing. Scopus. https://doi.org/10.1007/s10845-022-02069-x
Hubbs, C. D., Li, C., Sahinidis, N. V., Grossmann, I. E., & Wassick, J. M. (2020). A deep reinforcement learning approach for chemical production scheduling. Computers and Chemical Engineering, 141. Scopus. https://doi.org/10.1016/j.compchemeng.2020.106982
Kardos, C., Laflamme, C., Gallina, V., & Sihn, W. (2020). Dynamic scheduling in a job-shop production system with reinforcement learning. 97, 104–109. Scopus. https://doi.org/10.1016/j.procir.2020.05.210
Kim, B., & Maravelias, C. T. (2022). Supervised Machine Learning for Understanding and Improving the Computational Performance of Chemical Production Scheduling MIP Models. Industrial and Engineering Chemistry Research, 61(46), 17124–17136. Scopus. https://doi.org/10.1021/acs.iecr.2c02734
Kitchenham, B., & Charters, S. (2007). Guidelines for performing Systematic Literature Reviews in Software Engineering. 2.
Kusnadi, A., & Pratama, A. H. (2024). Data Analysis: A Comprehensive Guide to Data Analysis with Phyton (1st ed.). UMN Press.
Lang, S., Behrendt, F., Lanzerath, N., Reggelin, T., & Muller, M. (2020). Integration of Deep Reinforcement Learning and Discrete-Event Simulation for Real-Time Scheduling of a Flexible Job Shop Production. 2020-December, 3057–3068. Scopus. https://doi.org/10.1109/WSC48552.2020.9383997
Lee, S., Kim, J., Wi, G., Won, Y., Eun, Y., & Park, K. (2023). Deep Reinforcement Learning-Driven Scheduling in Multijob Serial Lines: A Case Study in Automotive Parts Assembly. IEEE Transactions on Industrial Informatics, 1–12. Scopus. https://doi.org/10.1109/TII.2023.3292538
Li, J., & Wang, C. (2023). Thoughts on Digital transformation and Evaluation of Enterprise Internal Control System Based on COSO Framework. Proceedings of the 2023 14th International Conference on E-Business, Management and Economics, 172–176. https://doi.org/10.1145/3616712.3616743
Luo, S. (2020). Dynamic scheduling for flexible job shop with new job insertions by deep reinforcement learning. Applied Soft Computing Journal, 91. Scopus. https://doi.org/10.1016/j.asoc.2020.106208
Marchesano, M. G., Guizzi, G., Popolo, V., & Converso, G. (2022). Dynamic scheduling of a due date constrained flow shop with Deep Reinforcement Learning. 55(10), 2932–2937. Scopus. https://doi.org/10.1016/j.ifacol.2022.10.177
Marchesano, M. G., Guizzi, G., Santillo, L. C., & Vespoli, S. (2021). Dynamic Scheduling in a Flow Shop Using Deep Reinforcement Learning. 630 IFIP, 152–160. Scopus. https://doi.org/10.1007/978-3-030-85874-2_16
Martínez Jiménez, Y., Coto Palacio, J., & Nowé, A. (2020). Multi-agent reinforcement learning tool for job shop scheduling problems. 1173 CCIS, 3–12. Scopus. https://doi.org/10.1007/978-3-030-41913-4_1
Muller, A., Grumbach, F., & Kattenstroth, F. (2024). Reinforcement Learning for Two-Stage Permutation Flow Shop Scheduling—A Real-World Application in Household Appliance Production. IEEE Access, 1–1. Scopus. https://doi.org/10.1109/ACCESS.2024.3355269
Ou, X., Chang, Q., Arinez, J., & Zou, J. (2018). Gantry Work Cell Scheduling through Reinforcement Learning with Knowledge-guided Reward Setting. IEEE Access, 6, 14699–14709. Scopus. https://doi.org/10.1109/ACCESS.2018.2800641
Paeng, B., Park, I.-B., & Park, J. (2021). Deep Reinforcement Learning for Minimizing Tardiness in Parallel Machine Scheduling with Sequence Dependent Family Setups. IEEE Access, 9, 101390–101401. Scopus. https://doi.org/10.1109/ACCESS.2021.3097254
Radjenović, D., Hericko, M., Torkar, R., & Živkovič, A. (2013). Software fault prediction metrics: A systematic literature review. Information and Software Technology, 55, 1397–1418. https://doi.org/10.1016/j.infsof.2013.02.009
Rummukainen, H., & Nurminen, J. K. (2019). Practical reinforcement learning—Experiences in lot scheduling application. 52(13), 1415–1420. Scopus. https://doi.org/10.1016/j.ifacol.2019.11.397
Schweitzer, F., Bitsch, G., & Louw, L. (2023). Choosing Solution Strategies for Scheduling Automated Guided Vehicles in Production Using Machine Learning. Applied Sciences (Switzerland), 13(2). Scopus. https://doi.org/10.3390/app13020806
Shiue, Y.-R., Lee, K.-C., & Su, C.-T. (2018). Real-time scheduling for a smart factory using a reinforcement learning approach. Computers and Industrial Engineering, 125, 604–614. Scopus. https://doi.org/10.1016/j.cie.2018.03.039
Song, L., Li, Y., & Xu, J. (2023). Dynamic Job-Shop Scheduling Based on Transformer and Deep Reinforcement Learning. Processes, 11(12). Scopus. https://doi.org/10.3390/pr11123434
Tejer, M., Szczepanski, R., & Tarczewski, T. (2024). Robust and efficient task scheduling for robotics applications with reinforcement learning. Engineering Applications of Artificial Intelligence, 127. Scopus. https://doi.org/10.1016/j.engappai.2023.107300
Togo, H., Asanuma, K., Nishi, T., & Liu, Z. (2022). Machine Learning and Inverse Optimization for Estimation of Weighting Factors in Multi-Objective Production Scheduling Problems. Applied Sciences (Switzerland), 12(19). Scopus. https://doi.org/10.3390/app12199472
Tremblet, D., Thevenin, S., & Dolgui, A. (2022). Predicting makespan in Flexible Job Shop Scheduling Problem using Machine Learning. 55(10), 1–6. Scopus. https://doi.org/10.1016/j.ifacol.2022.09.305
Unterkalmsteiner, M., Gorschek, T., Islam, A., Cheng, C., Permadi, R., & Feldt, R. (2011). Evaluation and Measurement of Software Process Improvement—A Systematic Literature Review. IEEE Transactions on Software Engineering, 38, 398–424. https://doi.org/10.1109/TSE.2011.26
Wang, H., Yan, Q., & Zhang, S. (2021). Integrated scheduling and flexible maintenance in deteriorating multi-state single machine system using a reinforcement learning approach. Advanced Engineering Informatics, 49. Scopus. https://doi.org/10.1016/j.aei.2021.101339
Wang, L., Hu, X., Wang, Y., Xu, S., Ma, S., Yang, K., Liu, Z., & Wang, W. (2021). Dynamic job-shop scheduling in smart manufacturing using deep reinforcement learning. Computer Networks, 190. Scopus. https://doi.org/10.1016/j.comnet.2021.107969
Wang, S., Li, J., Tang, H., & Wang, J. (2022). CEA-FJSP: Carbon emission-aware flexible job-shop scheduling based on deep reinforcement learning. Frontiers in Environmental Science, 10. Scopus. https://doi.org/10.3389/fenvs.2022.1059451
Wang, Y.-F. (2020). Adaptive job shop scheduling strategy based on weighted Q-learning algorithm. Journal of Intelligent Manufacturing, 31(2), 417–432. Scopus. https://doi.org/10.1007/s10845-018-1454-3
Wang, Z., & Liao, W. (2023). Smart scheduling of dynamic job shop based on discrete event simulation and deep reinforcement learning. Journal of Intelligent Manufacturing. Scopus. https://doi.org/10.1007/s10845-023-02161-w
Waschneck, B., Reichstaller, A., Belzner, L., Altenmuller, T., Bauernhansl, T., Knapp, A., & Kyek, A. (2018). Deep reinforcement learning for semiconductor production scheduling. 301–306. Scopus. https://doi.org/10.1109/ASMC.2018.8373191
Waschneck, B., Reichstaller, A., Belzner, L., Altenmüller, T., Bauernhansl, T., Knapp, A., & Kyek, A. (2018). Optimization of global production scheduling with deep reinforcement learning. 72, 1264–1269. Scopus. https://doi.org/10.1016/j.procir.2018.03.212
Wu, Z., Fan, H., Sun, Y., & Peng, M. (2023). Efficient Multi-Objective Optimization on Dynamic Flexible Job Shop Scheduling Using Deep Reinforcement Learning Approach. Processes, 11(7). Scopus. https://doi.org/10.3390/pr11072018
Zhang, J., & Cai, J. (2023). A Dual-Population Genetic Algorithm with Q-Learning for Multi-Objective Distributed Hybrid Flow Shop Scheduling Problem. Symmetry, 15(4). Scopus. https://doi.org/10.3390/sym15040836
Zhang, Y., Zhu, H., Tang, D., Zhou, T., & Gui, Y. (2022). Dynamic job shop scheduling based on deep reinforcement learning for multi-agent manufacturing systems. Robotics and Computer-Integrated Manufacturing, 78. Scopus. https://doi.org/10.1016/j.rcim.2022.102412
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Aries Harry Pratama
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
