Generation of synthetic manufacturing datasets for machine learning using discrete-event simulation

References

• Allen, T. T. (2011). Introduction to discrete event simulation and agent-based modeling: Voting systems, health care, military, and manufacturing. Springer Science & Business Media.
   Google Scholar
• Azadeh, A., Saberi, M., Kazem, A., Ebrahimipour, V., Nourmohammadzadeh, A., & Saberi, Z. (2013). A flexible algorithm for fault diagnosis in a centrifugal pump with corrupted data and noise based on ANN and support vector machine with hyper-parameters optimization. Applied Soft Computing, 13(3), 1478–1485. https://doi.org/10.1016/j.asoc.2012.06.020
   Web of Science ®Google Scholar
• Azevedo, A. I. R. L., & Santos, M. F. (2008). KDD, SEMMA and CRISP-DM: A parallel overview. IADS-DM.
   Google Scholar
• Bajari, P., Nekipelov, D., Ryan, S. P., & Yang, M. (2015). Machine learning methods for demand estimation. American Economic Review, 105(5), 481–485. https://doi.org/10.1257/aer.p20151021
   Web of Science ®Google Scholar
• Cheng, K., & Bateman, R. J. (2008). e-Manufacturing: Characteristics, applications and potentials. Progress in Natural Science, 18(11), 1323–1328. https://doi.org/10.1016/j.pnsc.2008.03.027
   Web of Science ®Google Scholar
• Cui, Y., Kara, S., & Chan, K. C. (2019, November). Large scale MTConnect data collection. In 2019 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM) (pp. 77–82). IEEE.
   Google Scholar
• Cui, Y., Kara, S., & Chan, K. C. (2020a). Manufacturing big data ecosystem: A systematic literature review. Robotics and computer-integrated Manufacturing, 62, 101861. https://doi.org/10.1016/j.rcim.2019.101861
   Web of Science ®Google Scholar
• Cui, Y., Kara, S., & Chan, K. C. (2020b, December). Monitoring and control of unstructured manufacturing big data. In 2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) (pp. 928–932). IEEE.
   Google Scholar
• Das, S. K., & Nagendra, P. (1997). Selection of routes in a flexible manufacturing facility. International Journal of Production Economics, 48(3), 237–247. https://doi.org/10.1016/S0925-5273(96)00106-5
   Web of Science ®Google Scholar
• Denkena, B., Schmidt, J., & Krüger, M. (2014). Data mining approach for knowledge-based process planning. Procedia Technology, 15, 406–415. https://doi.org/10.1016/j.protcy.2014.09.095
   Google Scholar
• El Emam, K., Mosquera, L., & Hoptroff, R. (2020). Practical synthetic data generation: Balancing privacy and the broad availability of data. O’Reilly Media.
   Google Scholar
• Ge, Z., Song, Z., Ding, S. X., & Huang, B. (2017). Data mining and analytics in the process industry: The role of machine learning. IEEE Access, 5, 20590–20616. https://doi.org/10.1109/ACCESS.2017.2756872
   Web of Science ®Google Scholar
• Greasley, A., & Edwards, J. S. (2021). Enhancing discrete-event simulation with big data analytics: A review. Journal of the Operational Research Society, 72(2), 247–267. https://doi.org/10.1080/01605682.2019.1678406
   Web of Science ®Google Scholar
• Gyulai, D., Kádár, B., & Monostori, L. (2014). Capacity planning and resource allocation in assembly systems consisting of dedicated and reconfigurable lines. Procedia CIRP, 25, 185–191. https://doi.org/10.1016/j.procir.2014.10.028
   Google Scholar
• Harun, K., & Cheng, K. (2012). An integrated modeling method for assessment of quality systems applied to aerospace manufacturing supply chains. Journal of Intelligent Manufacturing, 23(4), 1365–1378. https://doi.org/10.1007/s10845-010-0447-7
   Web of Science ®Google Scholar
• Huang, H. H., Pei, W., Wu, H. H., & May, M. D. (2013). A research on problems of mixed-line production and the re-scheduling. Robotics and Computer-Integrated Manufacturing, 29(3), 64–72. https://doi.org/10.1016/j.rcim.2012.04.014
   Web of Science ®Google Scholar
• Janssens, O., Slavkovikj, V., Vervisch, B., Stockman, K., Loccufier, M., Verstockt, S., Van de Walle, R., & Van Hoecke, S. (2016). Convolutional neural network based fault detection for rotating machinery. Journal of Sound and Vibration, 377, 331–345. https://doi.org/10.1016/j.jsv.2016.05.027
   Web of Science ®Google Scholar
• Kaylani, H., & Atieh, A. M. (2016). Simulation approach to enhance production scheduling procedures at a pharmaceutical company with large product mix. Procedia CIRP, 41, 411–416. https://doi.org/10.1016/j.procir.2015.12.072
   Google Scholar
• Koh, S. C. L., & Saad, S. M. (2003). MRP-controlled manufacturing environment disturbed by uncertainty. Robotics and Computer-Integrated Manufacturing, 19(1–2), 157–171. https://doi.org/10.1016/S0736-5845(02)00073-X
   Web of Science ®Google Scholar
• Kritzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W. (2018). Digital twin in manufacturing: A categorical literature review and classification. IFAC-PapersOnLine, 51(11), 1016–1022. https://doi.org/10.1016/j.ifacol.2018.08.474
   Google Scholar
• Lechevalier, D., Shin, S. J., Woo, J., Rachuri, S., & Foufou, S. (2015). A virtual milling machine model to generate machine-monitoring data for predictive analytics. In IFIP International Conference on Product Lifecycle Management (pp. 835–845). Springer.
   Google Scholar
• Lee, H., Kim, S. G., Park, H. W., & Kang, P. (2014). Pre-launch new product demand forecasting using the bass model: A statistical and machine learning-based approach. Technological Forecasting and Social Change, 86, 49–64. https://doi.org/10.1016/j.techfore.2013.08.020
   Web of Science ®Google Scholar
• Maas, S. L., & Standridge, C. R. (2005, December). Applying simulation to interative manufacturing cell design. In Proceedings of the Winter Simulation Conference, IEEE.
   Google Scholar
• Nyemba, W. R., & Mbohwa, C. (2017). Modelling, simulation and optimization of the materials flow of a multi-product assembling plant. Procedia Manufacturing, 8, 59–66. https://doi.org/10.1016/j.promfg.2017.02.007
   Google Scholar
• Pfeiffer, A., Gyulai, D., Kádár, B., & Monostori, L. (2016). Manufacturing lead time estimation with the combination of simulation and statistical learning methods. Procedia CIRP, 41, 75–80. https://doi.org/10.1016/j.procir.2015.12.018
   Google Scholar
• Priore, P., de la Fuente, D., Puente, J., & Parreño, J. (2006). A comparison of machine-learning algorithms for dynamic scheduling of flexible manufacturing systems. Engineering Applications of Artificial Intelligence, 19(3), 247–255. https://doi.org/10.1016/j.engappai.2005.09.009
   Web of Science ®Google Scholar
• Schröer, C., Kruse, F., & Gómez, J. M. (2021). A systematic literature review on applying CRISP-DM process model. Procedia Computer Science, 181, 526–534. https://doi.org/10.1016/j.procs.2021.01.199
   Google Scholar
• 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. https://doi.org/10.1016/j.engappai.2012.04.001
   Web of Science ®Google Scholar
• Shiue, Y. R. (2009). Data-mining-based dynamic dispatching rule selection mechanism for shop floor control systems using a support vector machine approach. International Journal of Production Research, 47(13), 3669–3690. https://doi.org/10.1080/00207540701846236
   Web of Science ®Google Scholar
• Silva, N., Ferreira, L. M. D., Silva, C., Magalhães, V., & Neto, P. (2017). Improving supply chain visibility with artificial neural networks. Procedia Manufacturing, 11, 2083–2090. https://doi.org/10.1016/j.promfg.2017.07.329
   Google Scholar
• Slack, N., Chambers, S., & Johnston, R. (2010). Operations management. Pearson education.
   Google Scholar
• Strickland, E. (2022, February 17). Are you still using real data to train your AI? IEEE Spectrum. https://spectrum.ieee.org/synthetic-data-ai
   Google Scholar
• Subramaniyan, M., Skoogh, A., Muhammad, A. S., Bokrantz, J., Johansson, B., & Roser, C. (2020). A generic hierarchical clustering approach for detecting bottlenecks in manufacturing. Journal of Manufacturing Systems, 55, 143–158. https://doi.org/10.1016/j.jmsy.2020.02.011
   Web of Science ®Google Scholar
• Subramaniyan, M., Skoogh, A., Salomonsson, H., Bangalore, P., Gopalakrishnan, M., & Sheikh Muhammad, A. (2018). Data-driven algorithm for throughput bottleneck analysis of production systems. Production & Manufacturing Research, 6(1), 225–246. https://doi.org/10.1080/21693277.2018.1496491
   Web of Science ®Google Scholar
• Van der Zee, D. J., & Van der Vorst, J. G. (2007, December). Guiding principles for conceptual model creation in manufacturing simulation. In 2007 Winter Simulation Conference, (pp. 776–784). IEEE.
   Google Scholar
• Weichert, D., Link, P., Stoll, A., Rüping, S., Ihlenfeldt, S., & Wrobel, S. (2019). A review of machine learning for the optimization of production processes. The International Journal of Advanced Manufacturing Technology, 104(5), 1889–1902. https://doi.org/10.1007/s00170-019-03988-5
   Web of Science ®Google Scholar
• Weimer, D., Scholz-Reiter, B., & Shpitalni, M. (2016). Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection. CIRP Annals, 65(1), 417–420. https://doi.org/10.1016/j.cirp.2016.04.072
   Web of Science ®Google Scholar
• Wirth, R., & Hipp, J. (2000, April). CRISP-DM: Towards a standard process model for data mining. In Proceedings of the 4th international conference on the practical applications of knowledge discovery and data mining, (Vol. 1). Springer-Verlag.
   Google Scholar
• Wu, X., & Zhu, X. (2008). Mining with noise knowledge: Error-aware data mining. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 38(4), 917–932. https://doi.org/10.1109/TSMCA.2008.923034
   Web of Science ®Google Scholar
• Wuest, T., Irgens, C., & Thoben, K. D. (2014). An approach to monitoring quality in manufacturing using supervised machine learning on product state data. Journal of Intelligent Manufacturing, 25(5), 1167–1180. https://doi.org/10.1007/s10845-013-0761-y
   Web of Science ®Google Scholar
• Wuest, T., Weimer, D., Irgens, C., & Thoben, K. D. (2016). Machine learning in manufacturing: Advantages, challenges, and applications. Production & Manufacturing Research, 4(1), 23–45. https://doi.org/10.1080/21693277.2016.1192517
   Web of Science ®Google Scholar
• Zhang, Y., Ma, S., Yang, H., Lv, J., & Liu, Y. (2018). A big data driven analytical framework for energy-intensive manufacturing industries. Journal of Cleaner Production, 197, 57–72. https://doi.org/10.1016/j.jclepro.2018.06.170
   Web of Science ®Google Scholar
• Zhuo, L., Chua Kim Huat, D., & Wee, K. H. (2012). Scheduling dynamic block assembly in shipbuilding through hybrid simulation and spatial optimisation. International Journal of Production Research, 50(20), 5986–6004. https://doi.org/10.1080/00207543.2011.639816
   Web of Science ®Google Scholar