Because of their cross-functional nature in the company, enhancing Production Planning and Control (PPC) functions can lead to a global improvement of manufacturing systems. With the advent of the Industry 4.0 (I4.0), copious availability of data, high-computing power and large storage capacity have made of Machine Learning (ML) approaches an appealing solution to tackle manufacturing challenges. As such, this paper presents a state-of-the-art of ML-aided PPC (ML-PPC) done through a systematic literature review analyzing 93 recent research application articles. This study has two main objectives: contribute to the definition of a methodology to implement ML-PPC and propose a mapping to classify the scientific literature to identify further research perspectives. To achieve the first objective, ML techniques, tools, activities, and data sources which are required to implement a ML-PPC are reviewed. The second objective is developed through the analysis of the use cases and the addressed characteristics of the I4.0. Results suggest that 75% of the possible research domains in ML-PPC are barely explored or not addressed at all. This lack of research originates from two possible causes: firstly, scientific literature rarely considers customer, environmental, and human-in-the-loop aspects when linking ML to PPC. Secondly, recent applications seldom couple PPC to logistics as well as to design of products and processes. Finally, two key pitfalls are identified in the implementation of ML-PPC models: the complexity of using Internet of Things technologies to collect data and the difficulty of updating the ML model to adapt it to the manufacturing system changes.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Price includes VAT (USA)
Tax calculation will be finalised during checkout.
Aissani, N., Bekrar, A., Trentesaux, D., & Beldjilali, B. (2012). Dynamic scheduling for multi-site companies: A decisional approach based on reinforcement multi-agent learning. Journal of Intelligent Manufacturing,23(6), 2513–2529.
Altaf, M. S., Bouferguene, A., Liu, H., Al-Hussein, M., & Yu, H. (2018). Integrated production planning and control system for a panelized home prefabrication facility using simulation and RFID. Automation in Construction,85, 369–383.
Bergmann, S., Feldkamp, N., & Strassburger, S. (2016). Approximation of dispatching rules for manufacturing simulation using data mining methods. In 2015 winter simulation conference (pp. 2329–2340). Huntington Beach, USA.
Bi, J., & Zhang, C. (2018). An empirical comparison on state-of-the-art multi-class imbalance learning algorithms and a new diversified ensemble learning scheme. Knowledge-Based Systems,158, 81–93.
Cai, B., Liu, H., & Xie, M. (2016). A real-time fault diagnosis methodology of complex systems using object-oriented Bayesian networks. Mechanical Systems and Signal Processing,80, 31–44.
Cao, X. C., Chen, B. Q., Yao, B., & He, W. P. (2019). Combining translation-invariant wavelet frames and convolutional neural network for intelligent tool wear state identification. Computers in Industry,106, 71–84.
Carvajal Soto, J. A., Tavakolizadeh, F., & Gyulai, D. (2019). An online machine learning framework for early detection of product failures in an Industry 4.0 context. International Journal of Computer Integrated Manufacturing,32(4–5), 452–465. https://doi.org/10.1080/0951192X.2019.1571238.
Chen, C., Xia, B., Zhou, B. H., & Xi, L. (2015). A reinforcement learning based approach for a multiple-load carrier scheduling problem. Journal of Intelligent Manufacturing,26(6), 1233–1245.
Curatolo, N., Lamouri, S., Huet, J. C., & Rieutord, A. (2014). A critical analysis of Lean approach structuring in hospitals. Business Process Management Journal,20(3), 433–454.
Diaz-Rozo, J., Bielza, C., & Larrañaga, P. (2017). Machine learning-based CPS for clustering high throughput machining cycle conditions. In: 45th SME North American manufacturing research conference (pp. 997–1008). Los Angeles, USA.
Ding, K., & Jiang, P. (2018). RFID-based production data analysis in an IoT-enabled smart job-shop. IEEE/CAA Journal of Automatica Sinica,5(1), 128–138.
Dinis, D., Barbosa-Póvoa, A., & Teixeira, Â. P. (2019). Valuing data in aircraft maintenance through big data analytics: A probabilistic approach for capacity planning using Bayesian networks. Computers & Industrial Engineering,128, 920–936.
Dolgui, A., Bakhtadze, N., Pyatetsky, V., Sabitov, R., Smirnova, G., Elpashev, D., & Zakharov, E. (2018). Data mining-based prediction of manufacturing situations data mining-based. In: 16th IFAC symposium on information control problems in manufacturing (pp. 316–321). Bergamo, Italy: Elsevier B.V.
Doltsinis, S., Ferreira, P., & Lohse, N. (2014). An MDP model-based reinforcement learning approach for production station ramp-up optimization: Q-learning analysis. IEEE Transactions on Systems, Man, and Cybernetics: Systems,44(9), 1125–1138.
Fotuhi, F., Huynh, N., Vidal, J. M., & Xie, Y. (2013). Modeling yard crane operators as reinforcement learning agents. Research in Transportation Economics,42(1), 3–12.
Gao, X., Shang, C., Jiang, Y., Huang, D., & Chen, T. (2014). Refinery scheduling with varying crude: A deep belief network classification and multimodel approach. AIChE Journal,60(7), 2525–2532.
Garengo, P., Biazzo, S., & Bititci, U. S. (2005). Performance measurement systems in SMEs: A review for a research agenda. International Journal of Management Reviews,7(1), 25–47.
Grabot, B. (2018). Rule mining in maintenance: Analysing large knowledge bases. Computers and Industrial Engineering, 139, 1–15.
Gyulai, D., Kádár, B., & Monosotori, L. (2015). Robust production planning and capacity control for flexible assembly lines. In 15th IFAC symposium on information control problems in manufacturing (pp. 2312–2317). Ottawa, Canada: Elsevier Ltd.
Gyulai, D., Kádár, B., & Monostori, L. (2014). Capacity planning and resource allocation in assembly systems consisting of dedicated and reconfigurable lines. In 8th international conference on digital enterprise technology (pp. 185–191). Stuttgart, Germany: Elsevier B.V.
Gyulai, D., Pfeiffer, A., Bergmann, J., & Gallina, V. (2018a). Online lead time prediction supporting situation-aware production control. In 6th CIRP global web conference—Envisaging the future manufacturing, design, technologies and systems in innovation era (pp. 190–195).
Gyulai, D., Pfeiffer, A., Nick, G., Gallina, V., Sihn, W., & Monostori, L. (2018b). Lead time prediction in a flow-shop environment with analytical and machine learning approaches. In 16th IFAC symposium on information control problems in manufacturing (pp. 1029–1034). Bergamo, Italy.
Habib Zahmani, M., & Atmani, B. (2018). Extraction of dispatching rules for single machine total weighted tardiness using a modified genetic algorithm and data mining. International Journal of Manufacturing Research,13(1), 1–25.
Hammami, Z., Mouelhi, W., & Ben Said, L. (2017). On-line self-adaptive framework for tailoring a neural-agent learning model addressing dynamic real-time scheduling problems. Journal of Manufacturing Systems,45, 97–108.
Hammami, Z., Mouelhi, W., & Said, L. B. (2016). A self adaptive neural agent based decision support system for solving dynamic real time scheduling problems. In 10th international conference on intelligent systems and knowledge engineering (pp. 494–501). Taipei, Taiwan.
Harding, J. A., Shahbaz, M., Srinivas, & Kusiak, A. (2006). Data Mining in Manufacturing: A Review. Journal of Manufacturing Science and Engineering-Transactions of the ASME,128(4), 969–976.
Heger, J., Branke, J., Hildebrandt, T., & Scholz-Reiter, B. (2016). Dynamic adjustment of dispatching rule parameters in flow shops with sequence-dependent set-up times. International Journal of Production Research,54(22), 6812–6824.
Hosseini, S., & Barker, K. (2016). A Bayesian network model for resilience-based supplier selection. International Journal of Production Economics,180, 68–87.
Hosseini, S., & Ivanov, D. (2019). A new resilience measure for supply networks with the ripple effect considerations: A Bayesian network approach. Annals of Operations Research, 1–27. https://doi.org/10.1007/s10479-019-03350-8.
Hosseini, S., Ivanov, D., & Dolgui, A. (2019). Review of quantitative methods for supply chain resilience analysis. Transportation Research Part E: Logistics and Transportation Review,125(December 2018), 285–307.
Huang, B., Wang, W., Ren, S., Zhong, R. Y., & Jiang, J. (2019). A proactive task dispatching method based on future bottleneck prediction for the smart factory. International Journal of Computer Integrated Manufacturing,32(3), 278–293.
Ji, W., & Wang, L. (2017). Big data analytics based fault prediction for shop floor scheduling. Journal of Manufacturing Systems,43, 187–194.
Jiang, S. L., Liu, M., Lin, J. H., & Zhong, H. X. (2016). A prediction-based online soft scheduling algorithm for the real-world steelmaking-continuous casting production. Knowledge-Based Systems,111, 159–172.
Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science,349(6245), 255–260.
Jurkovic, Z., Cukor, G., Brezocnik, M., & Brajkovic, T. (2018). A comparison of machine learning methods for cutting parameters prediction in high speed turning process. Journal of Intelligent Manufacturing,29(8), 1683–1693.
Kartal, H., Oztekin, A., Gunasekaran, A., & Cebi, F. (2016). An integrated decision analytic framework of machine learning with multi-criteria decision making for multi-attribute inventory classification. Computers & Industrial Engineering,101, 599–613.
Khader, N., & Yoon, S. W. (2018). Online control of stencil printing parameters using reinforcement learning approach. In 28th international conference on flexible automation and intelligent manufacturing (pp. 94–101). Columbus, USA: Elsevier B.V.
Kho, D. D., Lee, S., Zhong, R. Y. (2018). Big data analytics for processing time analysis in an IoT-enabled manufacturing shop floor. In 46th SME North American manufacturing research conference (pp. 1411–1420). Texas, USA: Elsevier B.V.
Kim, H., & Lim, D.-E. (2018). Deep-learning-based storage-allocation approach to improve the AMHS throughput capacity in a semiconductor fabrication facility. In Communications in computer and information science (pp. 232–240). Springer Singapore.
Kim, S., & Nembhard, D. A. (2013). Rule mining for scheduling cross training with a heterogeneous workforce. International Journal of Production Research,51(8), 2281–2300.
Kosmopoulos, D. I., Doulamis, N. D., & Voulodimos, A. S. (2012). Bayesian filter based behavior recognition in workflows allowing for user feedback. Computer Vision and Image Understanding,116(3), 422–434.
Kretschmer, R., Pfouga, A., Rulhoff, S., & Stjepandić, J. (2017). Knowledge-based design for assembly in agile manufacturing by using data mining methods. Advanced Engineering Informatics,33, 285–299.
Kruger, G. H., Shih, A. J., Hattingh, D. G., & Van Niekerk, T. I. (2011). Intelligent machine agent architecture for adaptive control optimization of manufacturing processes. Advanced Engineering Informatics,25(4), 783–796.
Kumar, A., Shankar, R., & Thakur, L. S. (2018). A big data driven sustainable manufacturing framework for condition-based maintenance prediction. Journal of Computational Science,27, 428–439.
Kusiak, A. (2017). Smart manufacturing must embrace big data. Nature,544(7648), 23–25.
Kusiak, A. (2019). Fundamentals of smart manufacturing: A multi-thread perspective. Annual Reviews in Control,47, 214–220.
Lai, L. K. C., & Liu, J. N. K. (2012). WIPA: Neural network and case base reasoning models for allocating work in progress. Journal of Intelligent Manufacturing,23(3), 409–421.
Lai, X., Shui, H., & Ni, J. (2018). A two-layer long short-term memory network for bottleneck prediction in multi-job manufacturing systems. In 13th international manufacturing science and engineering conference (p. V003T02A014). Texas, USA.
Lemieux, A. A., Lamouri, S., Pellerin, R., & Tamayo, S. (2015). Development of a leagile transformation methodology for product development. Business Process Management Journal,21(4), 791–819.
Leng, J., Chen, Q., Mao, N., & Jiang, P. (2018). Combining granular computing technique with deep learning for service planning under social manufacturing contexts. Knowledge-Based Systems,143, 295–306.
Li, D. C., Chen, C. C., Chen, W. C., & Chang, C. J. (2012a). Employing dependent virtual samples to obtain more manufacturing information in pilot runs. International Journal of Production Research,50(23), 6886–6903.
Li, X., Duan, F., Loukopoulos, P., Bennett, I., & Mba, D. (2018). Canonical variable analysis and long short-term memory for fault diagnosis and performance estimation of a centrifugal compressor. Control Engineering Practice,72(January), 177–191.
Li, X., Wang, J., & Sawhney, R. (2012b). Reinforcement learning for joint pricing, lead-time and scheduling decisions in make-to-order systems. European Journal of Operational Research,221(1), 99–109.
Li, L., Zijin, S., Jiacheng, N., & Fei, Q. (2013). Data-based scheduling framework and adaptive dispatching rule of complex manufacturing systems. International Journal of Advanced Manufacturing Technology,66(9–12), 1891–1905.
Liao, Q. (2018). Study of SVM-based intelligent dispatcher for parallel machines scheduling with sequence-dependent setup times. In 6th international conference on mechanical, automotive and materials engineering, CMAME 2018 (pp. 46–50). Hong Kong: IEEE.
Lieber, D., Stolpe, M., Konrad, B., Deuse, J., Morik, K. (2013). Quality prediction in interlinked manufacturing processes based on supervised & unsupervised machine learning. In 46th CIRP conference on manufacturing systems 2013 (pp. 193–198). Setúbal, Portugal: Elsevier B.V.
Lingitz, L., Gallina, V., Ansari, F., Gyulai, D., Pfeiffer, A., & Sihn, W. (2018). Lead time prediction using machine learning algorithms: A case study by a semiconductor manufacturer. In 51st CIRP conference on manufacturing systems (pp. 1051–1056). Stockholm, Sweden.
Llave, M. R. (2018). Data lakes in business intelligence: Reporting from the trenches. In CENTERIS/ProjMAN/HCist 2018 (pp. 516–524). Lisbon, Portugal: Elsevier B.V.
Lo Giudice, P., Musarella, L., Sofo, G., & Ursino, D. (2019). An approach to extracting complex knowledge patterns among concepts belonging to structured, semi-structured and unstructured sources in a data lake. Information Sciences,478, 606–626.
Lubosch, M., Kunath, M., & Winkler, H. (2018). Industrial scheduling with Monte Carlo tree search and machine learning. In 51st CIRP conference on manufacturing systems (pp. 1283–1287). Stockholm, Sweden: Elsevier B.V.
Lv, Y., Qin, W., Yang, J., & Zhang, J. (2018a). Adjustment mode decision based on support vector data description and evidence theory for assembly lines. Industrial Management and Data Systems,118(8), 1711–1726.
Lv, S., Zheng, B., Kim, H., & Yue, Q. (2018b). Data mining for material feeding optimization of printed circuit board template production. Journal of Electrical and Computer Engineering. https://doi.org/10.1155/2018/1852938.
Ma, Y., Qiao, F., Zhao, F., & Sutherland, J. (2017). Dynamic scheduling of a semiconductor production line based on a composite rule set. Applied Sciences,7(10), 1052.
Maghrebi, M., Shamsoddini, A., & Waller, S. T. (2016). Fusion based learning approach for predicting concrete pouring productivity based on construction and supply parameters. Construction Innovation,16(2), 185–202.
Manns, M., Wallis, R., & Deuse, J. (2015). Automatic proposal of assembly work plans with a controlled natural language. In 9th CIRP conference on intelligent computation in manufacturing engineering (pp. 345–350). Capri, Italy.
Manupati, V. K., Anand, R., Thakkar, J. J., Benyoucef, L., Garsia, F. P., & Tiwari, M. K. (2013). Adaptive production control system for a flexible manufacturing cell using support vector machine-based approach. International Journal of Advanced Manufacturing Technology,67(1–4), 969–981.
Mikołajczyk, A., & Grochowski, M. (2018). Data augmentation for improving deep learning in image classification problem. In 2018 international interdisciplinary PhD workshop, IIPhDW 2018 (pp. 117–122). Swinoujście, Poland: IEEE.
Mitchell, T. (1997). Machine learning (Vol. 2). New York: McGraw-Hill.
Moeuf, A., Pellerin, R., Lamouri, S., Tamayo-Giraldo, S., & Barbaray, R. (2018). The industrial management of SMEs in the era of Industry 4.0. International Journal of Production Research,56(3), 1118–1136.
Mori, J., & Mahalec, V. (2015). Planning and scheduling of steel plates production. Part I: Estimation of production times via hybrid Bayesian networks for large domain of discrete variables. Computers & Chemical Engineering,79, 113–134.
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.
Ou, X., Chang, Q., & Chakraborty, N. (2019). Simulation study on reward function of reinforcement learning in gantry work cell scheduling. Journal of Manufacturing Systems,50, 1–8.
Palombarini, J., & Martínez, E. (2012). SmartGantt—An intelligent system for real time rescheduling based on relational reinforcement learning. Expert Systems with Applications,39(11), 10251–10268.
Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering,22(10), 1345–1359.
Perez, L., & Wang, J. (2017). The effectiveness of data augmentation in image classification using deep learning. arXiv e-prints, arXiv:1712.04621.
Priore, P., Ponte, B., Puente, J., & Gómez, A. (2018). Learning-based scheduling of flexible manufacturing systems using ensemble methods. Computers & Industrial Engineering,126, 282–291.
Qu, S., Chu, T., Wang, J., Leckie, J., & Jian, W. (2015). A centralized reinforcement learning approach for proactive scheduling in manufacturing. In IEEE international conference on emerging technologies and factory automation. Luxembourg, Luxembourg.
Qu, S., Jie, W., & Shivani, G. (2016a). Learning adaptive dispatching rules for a manufacturing process system by using reinforcement learning approach. In IEEE international conference on emerging technologies and factory automation. Berlin, Germany.
Qu, S., Wang, J., Govil, S., & Leckie, J. O. (2016b). Optimized adaptive scheduling of a manufacturing process system with multi-skill workforce and multiple machine types: An ontology-based, multi-agent reinforcement learning approach. In 49th CIRP conference on manufacturing systems (CIRP-CMS 2016) (pp. 55–60). Stuttgart, Germany: Elsevier B.V.
Rainer, C. (2013). Data mining as technique to generate planning rules for manufacturing control in a complex production system. In K. Windt (Ed.), Robust manufacturing control. Heidelberg: Springer.
Reboiro-Jato, M., Glez-Dopazo, J., Glez, D., Laza, R., Gálvez, J. F., Pavón, R., et al. (2011). Using inductive learning to assess compound feed production in cooperative poultry farms. Expert Systems with Applications,38(11), 14169–14177.
Reuter, C., Brambring, F., Weirich, J., & Kleines, A. (2016). Improving data consistency in production control by adaptation of data mining algorithms. In 9th international conference on digital enterprise technology (pp. 545–550). Nanjing, China.
Rostami, H., Blue, J., & Yugma, C. (2018). Automatic equipment fault fingerprint extraction for the fault diagnostic on the batch process data. Applied Soft Computing,68, 972–989.
Ruessmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Engel, P., et al. (2015). Industry 4.0: The future of productivity and growth in manufacturing. The Boston Consulting Group,9, 54–89.
Sahebjamnia, N., Tavakkoli-Moghaddam, R., & Ghorbani, N. (2016). Designing a fuzzy Q-learning multi-agent quality control system for a continuous chemical production line—A case study. Computers & Industrial Engineering,93, 215–226.
Schuh, G., Prote, J. P., Luckert, M., & Hünnekes, P. (2017a). Knowledge discovery approach for automated process planning. In 50th CIRP conference on manufacturing systems knowledge (pp. 539–544). Taichung, Taiwan.
Schuh, G., Reinhart, G., Prote, J. P., Sauermann, F., Horsthofer, J., Oppolzer, F., & Knoll, D. (2019). Data mining definitions and applications for the management of production complexity. In 52nd CIRP conference on manufacturing systems (pp. 874–879). Ljubljana, Slovenia: Elsevier B.V.
Schuh, G., Reuter, C., Prote, J. P., Brambring, F., & Ays, J. (2017b). Increasing data integrity for improving decision making in production planning and control. CIRP Annals—Manufacturing Technology,66(1), 425–428.
Shahzad, A., & Mebarki, N. (2012). Data mining based job dispatching using hybrid simulation-optimization approach for shop scheduling problem. Engineering Applications of Artificial Intelligence,25(6), 1173–1181.
Sharp, M., Ak, R., & Hedberg, T. (2018). A survey of the advancing use and development of machine learning in smart manufacturing. Journal of Manufacturing Systems,48, 170–179.
Shiue, Y. R., Guh, R. S., & Tseng, T. Y. (2012). Study on shop floor control system in semiconductor fabrication by self-organizing map-based intelligent multi-controller. Computers & Industrial Engineering,62(4), 1119–1129.
Shiue, Y. R., Lee, K. C., & Su, C. T. (2018). Real-time scheduling for a smart factory using a reinforcement learning approach. Computers & Industrial Engineering,125(101), 604–614.
Solti, A., Raffel, M., Romagnoli, G., & Mendling, J. (2018). Misplaced product detection using sensor data without planograms. Decision Support Systems,112, 76–87.
Stein, N., Meller, J., & Flath, C. M. (2018). Big data on the shop-floor: Sensor-based decision-support for manual processes. Journal of Business Economics,88(5), 593–616.
Stricker, N., Kuhnle, A., Sturm, R., & Friess, S. (2018). Reinforcement learning for adaptive order dispatching in the semiconductor industry. CIRP Annals—Manufacturing Technology,67(1), 511–514.
Talhi, A., Fortineau, V., Huet, J. C., & Lamouri, S. (2017). Ontology for cloud manufacturing based product lifecycle management. Journal of Intelligent Manufacturing,30(5), 1–22.
Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018). Data-driven smart manufacturing. Journal of Manufacturing Systems,48, 157–169.
Thomas, T. E., Koo, J., Chaterji, S., & Bagchi, S. (2018b). Minerva: A reinforcement learning-based technique for optimal scheduling and bottleneck detection in distributed factory operations. In 10th international conference on communication systems and networks (pp. 129–136). Bengaluru, India.
Thomas, A., Noyel, M., Zimmermann, E., Suhner, M.-C., Bril El Haouzi, H., & Thomas, P. (2018a). Using a classifier ensemble for proactive quality monitoring and control: The impact of the choice of classifiers types, selection criterion, and fusion process. Computers in Industry,99(March), 193–204.
Tian, G., Zhou, M., & Chu, J. (2013). A chance constrained programming approach to determine the optimal disassembly sequence. IEEE Transactions on Automation Science and Engineering,10(4), 1004–1013.
Tong, Y., Li, J., Li, S., & Li, D. (2016). Research on energy-saving production scheduling based on a clustering algorithm for a forging enterprise. Sustainability, 8(2), Article number 136.
Tony Arnold, J. R., Chapman, S. N., & Clive, L. M. (2012). Introduction to materials management (Vol. 118). Pearson: London.
Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management,14, 207–222.
Tuncel, E., Zeid, A., & Kamarthi, S. (2014). Solving large scale disassembly line balancing problem with uncertainty using reinforcement learning. Journal of Intelligent Manufacturing,25(4), 647–659.
Wang, C., & Jiang, P. (2018). Manifold learning based rescheduling decision mechanism for recessive disturbances in RFID-driven job shops. Journal of Intelligent Manufacturing,29(7), 1485–1500.
Wang, C., & Jiang, P. (2019). Deep neural networks based order completion time prediction by using real-time job shop RFID data. Journal of Intelligent Manufacturing,30(3), 1303–1318.
Wang, H., Jiang, Z., Zhang, X., Wang, Y., & Wang, Y. (2017). A fault feature characterization based method for remanufacturing process planning optimization. Journal of Cleaner Production,161, 708–719.
Wang, J., Ma, Y., Zhang, L., Gao, R. X., & Wu, D. (2018a). Deep learning for smart manufacturing: Methods and applications. Journal of Manufacturing Systems,48, 144–156.
Wang, C. L., Rong, G., Weng, W., & Feng, Y. P. (2015). Mining scheduling knowledge for job shop scheduling problem. In 15th IFAC symposium on information control problems in manufacturing (pp. 800–805). Ottawa, Canada: Elsevier Ltd.
Wang, H. X., & Yan, H. Sen. (2016). An interoperable adaptive scheduling strategy for knowledgeable manufacturing based on SMGWQ-learning. Journal of Intelligent Manufacturing,27(5), 1085–1095.
Wang, J., Yang, J., Zhang, J., Wang, X., & Zhang, W. (2018b). Big data driven cycle time parallel prediction for production planning in wafer manufacturing. Enterprise Information Systems,12(6), 714–732.
Wang, J., Zhang, J., & Wang, X. (2018c). Bilateral LSTM: A two-dimensional long short-term memory model with multiply memory units for short-term cycle time forecasting in re-entrant manufacturing systems. IEEE Transactions on Industrial Informatics,14(2), 748–758.
Waschneck, B., Bauernhansl, T., Knapp, A., Kyek, A. (2018). Optimization of global production scheduling with deep reinforcement learning. In 51st CIRP conference on manufacturing systems (pp. 1264–1269). Stockholm, Sweden.
Wauters, T., Verbeeck, K., Verstraete, P., Vanden Berghe, G., & De Causmaecker, P. (2012). Real-world production scheduling for the food industry: An integrated approach. Engineering Applications of Artificial Intelligence,25(2), 222–228.
Wu, W., Ma, Y., Qiao, F., & Gu, X. (2015). Data mining based dynamic scheduling approach for semiconductor manufacturing system. In 34th Chinese control conference (pp. 2603–2608). Hangzhou, China.
Xanthopoulos, A. S., Kiatipis, A., Koulouriotis, D. E., & Stieger, S. (2017). Reinforcement learning-based and parametric production-maintenance control policies for a deteriorating manufacturing system. IEEE Access,6, 576–588.
Yang, Z., Zhang, P., & Chen, L. (2016). RFID-enabled indoor positioning method for a real-time manufacturing execution system using OS-ELM. Neurocomputing,174, 121–133.
Yeh, D. Y., Cheng, C. H., & Hsiao, S. C. (2011). Classification knowledge discovery in mold tooling test using decision tree algorithm. Journal of Intelligent Manufacturing,22(4), 585–595.
Yuan, B., Wang, L., & Jiang, Z. (2014). Dynamic parallel machine scheduling using the learning agent. In 2013 IEEE international conference on industrial engineering and engineering management (pp. 1565–1569). Bangkok, Thailand.
Zellner, G. (2011). A structured evaluation of business process improvement approaches. Business Process Management Journal,17(2), 203–237.
Zhang, Z., Zheng, L., Hou, F., & Li, N. (2011). Semiconductor final test scheduling with Sarsa(λ, k) algorithm. European Journal of Operational Research,215(2), 446–458.
Zhang, Z., Zheng, L., Li, N., Wang, W., Zhong, S., & Hu, K. (2012). Minimizing mean weighted tardiness in unrelated parallel machine scheduling with reinforcement learning. Computers & Operations Research,39(7), 1315–1324.
Zhong, R. Y., Huang, G. Q., Dai, Q. Y., & Zhang, T. (2014). Mining SOTs and dispatching rules from RFID-enabled real-time shopfloor production data. Journal of Intelligent Manufacturing,25(4), 825–843.
Zhong, R. Y., Newman, S. T., Huang, G. Q., & Lan, S. (2016). Big Data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives. Computers & Industrial Engineering,101, 572–591.
Zhou, P., Guo, D., & Chai, T. (2018). Data-driven predictive control of molten iron quality in blast furnace ironmaking using multi-output LS-SVR based inverse system identification. Neurocomputing,308, 101–110.
Zhou, L., Pan, S., Wang, J., & Vasilakos, A. V. (2017). Machine learning on big data: Opportunities and challenges. Neurocomputing,237(January), 350–361.
This work was financially supported by a partnership between the company iFAKT France SAS and the ANRT (Association Nationale de la Recherche et de la Technologie) under the Grant 2018/1266. Furthermore, the authors thank the Editor-in-chief and three anonymous referees who helped improve the quality of this paper through their comments and suggestions.
Conflict of interest
The authors declare that they have no conflict of interest.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Usuga Cadavid, J.P., Lamouri, S., Grabot, B. et al. Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0. J Intell Manuf 31, 1531–1558 (2020). https://doi.org/10.1007/s10845-019-01531-7
- Machine learning
- Industry 4.0
- Smart manufacturing
- Production planning and control
- Systematic literature review