References
- Alam, K. M., & Saddik, A. E. (2017). C2PS: A digital twin architecture reference model for the cloud-based cyber-physical systems. IEEE Access, 5(1), 2050–2062. https://doi.org/https://doi.org/10.1109/ACCESS.2017.2657006
- Ashrafian, A., Pettersen, O., Kuntze, K. N., Franke, J., Alfnes, E., Henriksen, K. F., & Spone, J. (2019). Full-scale discrete event simulation of an automated modular conveyor system for warehouse logistics. Conference on Advances in Production Management Systems - APMS. Proceedings of 2019 Conference on Advances in Production Management Systems. (pp. 1–8). Austin. https://doi.org/https://doi.org/10.1007/978-3-030-29996-5_4
- Balci, O. (2012). A life cycle for modeling and simulation. Simulation, 88(7), 870–883. https://doi.org/https://doi.org/10.1177/0037549712438469
- Banks, J., Carson, I. J., Nelson, S., & Nicol, D. M. (2010). Discrete event system simulation (Vol. 5). Pearson.
- Barlas, P., & Heavey, C. (2016). Automation of input data to discrete event simulation for manufacturing: A review. International Journal of Modeling Simulation and Scientific Computing, 07(1), 1–27. https://doi.org/https://doi.org/10.1142/S1793962316300016
- Beregi, R., Szaller, Á., & Kádár, B. (2018). Snergy of multi-modelling for process control. IFAC PapersOnLine, 51(11), 1023–1028. https://doi.org/https://doi.org/10.1016/j.ifacol.2018.08.473
- Chwif, L., & Medina, A. C. (2015). Modelagem e Simulação de Eventos Discretos [Discrete event modelling and simulation] (4 ed.). Elsevier.
- Donhauser, T., Ebersbanch, T., Franke, J., & Schuderer, P. (2018). Rolling-reactive optimization of production process in a calcium silicate masonry unit plant using online simulation. Procedia CIRP, 72(1), 249–254. https://doi.org/https://doi.org/10.1016/j.procir.2018.03.266
- Eyre, J. M., Dodd, T. J., Freeman, C., Lanyon-Hogg, R., Lockwood, A. J., & Scott, R. W. (2018). Demonstration of an industrial framework for an implementation of a process digital twin. International Mechanical Engineering Congress and Exposition. Proceedings of 2018 International Mechanical Engineering Congress and Exposition. (pp. 1–9). Pittsburgh. https://doi.org/https://doi.org/10.1115/IMECE2018-87361
- Greasley, A., & Owen, C. (2018). Modelling people’ s behavior using discrete-event simulation: A review. International Journal of Operations & Production Management, 38(5), 1228–1244. https://doi.org/https://doi.org/10.1108/IJOPM-10-2016-0604
- Grube, D., Malik, A. A., & Bilberg, A. (2019). SMEs can touch Industry 4.0 in the smart learning factory. Procedia Manufacturing, 31(1), 219–224. https://doi.org/https://doi.org/10.1016/j.promfg.2019.03.035
- 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/https://doi.org/10.1016/j.ifacol.2018.08.474
- Kunath, M., & Winkler, H. (2018). Integrating the Digital Twin of the manufacturing system into a decision support system for improving the order management process. Procedia CIRP, 72(1), 225–231. https://doi.org/https://doi.org/10.1016/j.procir.2018.03.192
- Law, A. M. (2009). How to build valid and credible simulation models. Winter Simulation Conference. Proceedings of 2009 Winter Simulation Conference. (pp. 24–33). Austin. https://doi.org/https://doi.org/10.1109/WSC.2009.5429312
- Leal, F. (2008). Analysis of the interactive effect of failures in manufacturing processes through the design of simulated experiments [Doctoral dissertation, Paulista State University- UNESP]. UNESP Institutional Repository. https://repositorio.unesp.br/handle/11449/106417
- Lu, Y., Min, Q., Liu, Z., & Yang, Y. (2019). An IoT-enabled simulation approach for process planning and analysis: A case from engine re-manufacturing industry. International Journal of Computer Integrated Manufacturing, 32(4), 413429. https://doi.org/https://doi.org/10.1080/0951192X.2019.1571237
- Mieth, C., Meyer, A., & Henke, M. (2019). Framework for the usage of data from real-time indoor localization systems to derive inputs for manufacturing simulation. Procedia CIRP, 81 (1), 868–873. https://doi.org/https://doi.org/10.1016/j.procir.2019.03.216
- Montevechi, J. A. B., Leal, F., Pinho, A. F., Costa, R. F., Oliveira, M. L., & Silva, A. L. F. (2010). Conceptual modeling in simulation projects by mean adapted IDEF: An application in a Brazilian tech company. Winter Simulation Conference. Proceedings of 2010 Winter Simulation Conference. (pp. 1624–1635). Baltimore. https://doi.org/https://doi.org/10.1109/WSC.2010.5678908
- Montevechi, J. A. B., Pereira, T. F., Silva, C. E. S., Miranda, R. C., & Scheidegger, A. P. G. (2015). Identification of the main methods used in simulation projects. Winter Simulation Conference. Proceedings of 2015 Winter Simulation Conference. (pp. 3469–3480). Huntington Beach. https://doi.org/https://doi.org/10.1109/WSC.2015.7408507
- Montgomery, D. C., & Runger, G. C. (2011). Applied statistics and probability for engineers (Vol. 5). John Wiley & Sons.
- Mourtzis, D. (2020). Simulation in the design and operation of manufacturing systems: State of the art and new trends. International Journal of Production Research, 58(7), 1927–1949. https://doi.org/https://doi.org/10.1080/00207543.2019.1636321
- Murphy, A., Taylor, C., Acheson, C., Utterfield, J., Jin, Y., Higgins, P., Collins, R., & Higgins, C. (2020). Representing financial data streams in digital simulations to support data flow design for a future digital twin. Robotics and Computer Integrated Manufacturing, 61(1), 1–16. https://doi.org/https://doi.org/10.1016/j.rcim.2019.101853
- Negahban, A., & Smith, J. S. (2014). Simulation for manufacturing system design and operation: Literature review and analysis. Journal of Manufacturing Systems, 33(1), 241–261. https://doi.org/https://doi.org/10.1016/j.jmsy.2013.12.007
- Onggo, B. S., Mustafee, N., Smart, A., Juan, A. A., & Molloy, O. (2018). Symbiotic simulation system: Hybrid systems model meets big data analytics. Winter Simulation Conference. Proceedings of 2018 Winter Simulation Conference. (pp. 1358–1369). Gothenburg. https://doi.org/https://doi.org/10.1109/WSC.2018.8632407
- Rodič, B. (2017). Industry 4.0 and the new simulation modelling paradigm. Organizacija, 50(1), 193–207. https://doi.org/https://doi.org/10.1515/orga-2017-0017
- Rüttimann, B. G., & Stöckli, M. T. (2016). Lean and industry 4.0 - twins, partners, or contenders? A due clarification regarding the supposed clash of two production systems. Journal of Service Science and Management, 9(1), 485–500. https://doi.org/https://doi.org/10.4236/jssm.2016.96051
- Sargent, R. G. (2013). Verification and validation of simulation models. Journal of Simulation, 7(1), 12–24. https://doi.org/https://doi.org/10.1057/jos.2012.20
- Shafto, M., Conroy, M., Doyle, R., Glaessgen, E., Klemp, C., LeMoigne, J., & Wang., L. (2010). DRAFT modeling, simulation, information technology & processing roadmap. Technology Area 11 - National Aeronautics and Space Administration (NASA): 1–27.
- Skoogh, A., Perera, T., & Johansson, B. (2012). Simulation modelling practice and theory input data management in simulation – industrial practices and future trends. Simulation Modelling Practice and Theory, 29(1), 181–192. https://doi.org/https://doi.org/10.1016/j.simpat.2012.07.009
- Tao, F., & Zhang, M. (2017). Digital twin shop-floor: A new shop-floor paradigm towards smart manufacturing. IEEE Access, 5(1), 20418–20427. https://doi.org/https://doi.org/10.1109/ACCESS.2017.2756069
- Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., & Sui, F. (2018). Digital twin-driven product design, manufacturing and service with big data. The International Journal of Advanced Manufacturing Technology, 94(1), 3563–3576. https://doi.org/https://doi.org/10.1007/s00170-017-0233-1
- Terkaj, W., Gaboardi, P., Trevisan, C., Tolio, T., & Urgo, M. (2019). A digital factory platform for the design of roll shop plants. CIRP Journal of Manufacturing Science and Technology, 26(1), 88–93. https://doi.org/https://doi.org/10.1016/j.cirpj.2019.04.007
- Uhlemann, T. H., Lehmann, C., & Steinhilper, R. (2017). The digital twin: Realizing the cyber-physical production system for industry 4.0. Procedia CIRP, 61(1), 335–340. https://doi.org/https://doi.org/10.1016/j.procir.2016.11.152
- Uriarte, A. G., Ng, A. H., & Moris, M. U. (2018). Supporting the lean journey with simulation and optimization in the context of Industry 4.0. Procedia Manufacturing, 25(1), 586–593. https://doi.org/https://doi.org/10.1016/j.promfg.2018.06.097
- Vachálek, J., Bartalský, L., Rovný, O., Šišmišová, D., Morháč, M., & Lokšík, M. (2017). The digital twin of an industrial production line within the industry 4.0 concept. International Conference on Process Control. Proceedings of 21st International Conference on Process Control. (pp. 258–262). Slovakia. https://doi.org/https://doi.org/10.1109/PC.2017.7976223
- Vijayakumar, K., Dhanasekaran, C., Pugazhenthi, R., & Sivaganesan, S. (2019). Digital twin for factory system simulation. International Journal of Recent Technology and Engineering, 8(1), 63–68.
- Wan, J., Cai, H., & Zhou, K. (2015). Industrie 4.0: Enabling technologies. International Conference on Intelligent Computing and Internet of Things. Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things. (pp. 135–140). https://doi.org/https://doi.org/10.1109/ICAIOT.2015.7111555
- Wright, L., & Davidson, S. (2020). How to tell the difference between a model and a digital twin. Advanced Modeling and Simulation in Engineering Sciences, 7(13), 1–13. https://doi.org/https://doi.org/10.1186/s40323-020-00147-4
- Xu, L. D., Xu, E. L., & Li, L. (2018). Industry 4.0: State of the art and future trends. International Journal of Production Research, 56(8), 1–22. https://doi.org/https://doi.org/10.1080/00207543.2018.1444806
- Zhong, R. Y., Xu, X., Klotz, E., & Newman, S. T. (2017). Intelligent manufacturing in the context of industry 4.0: A review. Engineering, 3(1), 616–630. https://doi.org/https://doi.org/10.1016/J.ENG.2017.05.015
- Zhuang, C., Liu, J., & Xiong, H. (2018). Digital twin-based smart production management and control framework for the complex product assembly shop-floor. The International Journal of Advanced Manufacturing Technology, 96(1), 1149–1163. https://doi.org/https://doi.org/10.1007/s00170-018-1617-6