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Warianty tytułu
Języki publikacji
Abstrakty
The article discusses the application of computer simulation in the optimization of production pro-cesses, particularly in the context of analyzing scenarios related to the addition of new production lines. The conducted research and simulations have shown that computer simulation is a key tool for precise modeling and analysis of various options, allowing for better understanding and optimization of production activities. The article presents the theoretical foundations of simulation along with prac-tical examples of its application, focusing on assessing the impact of different production line config-urations on the overall system’s efficiency. The analysis of benefits includes shortening the production cycle time, increasing flexibility, and improving operational efficiency. The challenges associated with implementing computer simulation, such as the need for specialized knowledge and the necessity for continuous updates of simulation models, are also discussed. Based on the research and analyses con-ducted, the article demonstrates that computer simulation is an effective tool supporting strategic and operational decision-making in production management, particularly in the context of expanding pro-duction infrastructure.
Czasopismo
Rocznik
Tom
Strony
520--527
Opis fizyczny
Bibliogr. 25 poz., rys., tab.
Twórcy
autor
- Silesia University of Technology, 40-019 Katowice, Krasińskiego st 8, Poland
autor
- Silesia University of Technology, 40-019 Katowice, Krasińskiego st 8, Poland
Bibliografia
- 1. Attaran, M., 2022. The Impact of Digital Twins on the Evolution of Intelligent Manufacturing and Industry 4.0. Advances in Computational Intelli-gence. DOI: 10.1007/s00542-021-06244-3.
- 2. Catarci, T., Firmani, D., Leotta, F., Mandreoli, F., Mecella, M., Sapio, F., 2019. A conceptual architecture and model for smart manufacturing re-lying on service-based digital twins. In: 2019 IEEE International Confer-ence on Web Services (ICWS). DOI: 10.1109/ICWS.2019.00032.
- 3. Chen, R., Huang, Y., Zhao, L., 2022. Digital Twin Applications: A Survey of Recent Advances and Challenges. Processes, 10(4), 589. DOI: 10.3390/pr10040589.
- 4. Cheng, J., Zhang, H., Tao, F., Juang, C.F., 2020. DT-II: Digital twin enhanced Industrial Internet reference framework towards smart manufacturing. Robotics and Computer-Integrated Manufacturing. DOI: 10.1016/j.rcim.2019.101881.
- 5. Ciano, M.P., Pozzi, R., Rossi, T., Strozzi, F., 2020. Digital twin-enabled smart industrial systems: A bibliometric review. International Journal of Com-puter Integrated Manufacturing. DOI: 10.1080/0951192X.2020.1852600.
- 6. Cimino, C., Negri, E., Fumagalli, L., 2019. Review of digital twin applica-tions in manufacturing. Computers in Industry. DOI: 10.1016/j.com-pind.2019.103130.
- 7. Coelho, F., Relvas, S., Barbosa-Póvoa, A.P., 2021. Simulation-based decision support tool for in-house logistics: The basis for a digital twin. Computers and Industrial Engineering. DOI: 10.1016/j.cie.2020.107094.
- 8. Frantzén, M., Ng, A.H.C., Moore, P., 2011. A simulation-based scheduling system for real-time optimization and decision-making support. Robotics and Computer-Integrated Manufacturing, 27(4), 696–705. DOI: 10.1016/j.rcim.2010.12.001.
- 9. Gao, Y., Zhang, L., Wang, M., 2023. Simulation-Based Manufacturing Sys-tem Design and Analysis. Springer. DOI: 10.1007/978-981-16-9590-3.
- 10. Karwat, B., Rubacha, P., Stańczyk, E., 2022. Numerical Simulations of the Exploitation Parameters of the Rotary Feeder. Management Systems in Production Engineering, 30(4), pp. 348-354. DOI: 10.2478/mspe-2022-0044.
- 11. Kelton, W.D., Sadowski, R.P., Sturrock, D.T., 2015. Simulation with Arena. McGraw-Hill Education.
- 12. Kusiak, A., 2020. Convolutional and generative adversarial neural networks in manufacturing. International Journal of Production Research, 58, 1594–1604. DOI: 10.1080/00207543.2019.1681537.
- 13. Law, A.M., 2014. Simulation Modeling and Analysis. McGraw-Hill Educa-tion.
- 14. Lee, J., Kim, B.H., 2023. Digital Twin for Smart Steel Manufacturing. Journal of Industrial and Production Engineering. DOI: 10.1080/21681015.2022.2045421.
- 15. Mandolla, C., et al., 2022. Digital Twin Applications in Manufacturing: A Comprehensive Review. Journal of Manufacturing Systems. DOI: 10.1016/j.jmsy.2021.10.006.
- 16. Park, K.T., Lee, D., Do Noh, S., 2020. Operation procedures of a work-center-level digital twin for sustainable and smart manufacturing. International Journal of Precision Engineering and Manufacturing-Green Technology, 7, 791–814. DOI: 10.1007/s40684-020-00257-6.
- 17. Sargent, R.G., 2013. Verification and Validation of Simulation Models. Jour-nal of Simulation, 7(1), 12-24. DOI: 10.1057/jos.2012.20.
- 18. Sari, M.W., Herianto, I.G.B.B., Dharma, A.E.T., 2022. Integrated Production System on Social Manufacturing: A Simulation Study. Management Sys-tems in Production Engineering, 30(3), pp. 230-237. DOI: 10.2478/mspe-2022-0029.
- 19. Seňová, A., Pavolová, H., Škvareková, E., 2023. Assessment of the Impact of Working Risks in the Exploitation of Raw Materials. Management Sys-tems in Production Engineering, 31(1), pp. 45-53. DOI: 10.2478/mspe-2023-0009.
- 20. Sharma, P., Mathur, S., 2022. Recent Advances in Manufacturing Modelling and Optimization. Springer. DOI: 10.1007/978-981-16-9403-6.
- 21. Tan, X., Wang, Y., 2022. Digital Twin and Steel Manufacturing. International Journal of Production Research, 60(12), 3710-3722. DOI: 10.1080/00207543.2021.1956789.
- 22. Uhlenkamp, J.-F., Hribernik, K., Wellsandt, S., Thoben, K.-D., 2019. Digital twin applications: a first systemization of their dimensions. In: 2019 IEEE International Conference on Engineering, Technology and Innova-tion (ICE/ITMC), pp. 1–8. DOI: 10.1109/ICE.2019.8792658.
- 23. Zhang, C., Zhou, G., Hu, J., Li, J., 2020. Deep learning-enabled intelligent process planning for digital twin manufacturing cell. Knowledge-Based Systems, 191, 105247. DOI: 10.1016/j.knosys.2019.105247.
- 24. Zhang, J., Li, H., 2022. Optimizing Steel Profile Production Using Discrete Event Simulation. Journal of Manufacturing Processes, 62, 548-556. DOI: 10.1016/j.jmapro.2021.10.003.
- 25. Zheng, P., Wang, Z., Chen, C.H., Khoo, L.P., 2020. A Systematic Design Ap-proach to Enhance the Development of Flexible and Robust Manufactur-ing Systems Using Digital Twin Technology. Computers & Industrial En-gineering, 139, 106230. DOI: 10.1016/j.cie.2019.106230.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c3e5b1ab-2f24-4af5-822e-1e4e5845f133
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