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Background: The article discusses the problem of modeling and simulation of the production process, as well as accompanying intralogistics processes in the automotive industry, which are characterized by advanced production standards and high product quality. The analyzed processes concern a Flexible Manufacturing System (FMS) for manufacturing a family of gear wheels and a distribution center that operates in a cross-docking regime. The goal of the research is to create a methodology of modeling and simulation of production and intralogistics processes with the help of a digital twin, which aims to integrate the design of production and logistics systems, considering factors such as manipulation and transportation of each product. Therefore, the article emphasizes the optimization of intralogistics processes. Methods: The proposed methodology is based on the Value Stream Mapping (VSM) method, which is used for identifying critical components of the production system being analyzed. The first step involves creating a VSM of the current state of the production system, which can be utilized to model key research areas, such as current planning, scheduling, manufacturing, and intralogistics. Analyzing the map and related models leads to the development of a future state map, used to create improved models of the system. Subsequently, the models are integrated into a digital twin with the use of the Flexsim program. Results: The results of simulation experiments confirm that the introduction of a FMS with AGV transport allows improvement in the intralogistics system, reduces the production cycle of a planned order package from two days to one day, and improves the distribution process with the use of pallets and short transportation batches, which enables 64 shipments during one shift. The experiments also confirm the stability of the model for different numbers of AGVs and a random inflow of orders. Conclusions: The proposed methodology of creating a digital twin makes it possible to evaluate the effectiveness of the proposed improvements and to verify production plans. The advantage of the applied simulations and analyses is the significant reduction in the amount of time spent searching for optimal solutions and storage capacities.
Wydawca
Czasopismo
Rocznik
Tom
Strony
401--414
Opis fizyczny
Bibliogr. 28 poz., fot., rys., tab.
Twórcy
autor
- Department of Engineering Processes Automation and Integrated Manufacturing Systems Silesian, University of Technology, Gliwice, Poland
autor
- Department of Engineering Processes Automation and Integrated Manufacturing Systems Silesian, University of Technology, Gliwice, Poland
autor
- Department of Engineering Processes Automation and Integrated Manufacturing Systems Silesian, University of Technology, Gliwice, Poland
autor
- Department of Engineering Processes Automation and Integrated Manufacturing Systems Silesian, University of Technology, Gliwice, Poland
autor
- Department of Engineering Processes Automation and Integrated Manufacturing Systems Silesian, University of Technology, Gliwice, Poland
autor
- Department of Engineering Processes Automation and Integrated Manufacturing Systems Silesian, University of Technology, Gliwice, Poland
autor
- Department of Engineering Processes Automation and Integrated Manufacturing Systems Silesian, University of Technology, Gliwice, Poland
autor
- Department of Mining Mechanization and Robotisation Silesian, University of Technology, Gliwice, Poland
Bibliografia
- 1. Balon B. Kalinowski K., Paprocka I., 2023, Production Planning Using a Shared Resource Register Organized According to the Assumptions of Blockchain Technology. Sensors 23(4), 2308, 1-22. https://www.doi.org/10.3390/s23042308
- 2. Burduk A., 2014, “Stability Analysis of the Production System Using Simulation Models”, in Pawlewski P., Greenwood A. (Eds.), Process Simulation and Optimization in Sustainable Logistics and Manufacturing, EcoProduction, Springer, Cham., Switzerland, 69-83. https://www.doi.org/10.1007/978-3-319-07347-7_5
- 3. Dulebenets, M., 2018, A Diploid Evolutionary Algorithm for Sustainable Truck Scheduling at a Cross-Docking Facility. Sustainability 10(5), 1333, 1-23. https://www.doi.org/10.3390/su10051333
- 4. Foit K., Gołda G., Kampa A., 2020, Integration and Evaluation of Intra-Logistics Processes in Flexible Production Systems Based on OEE Metrics, with the Use of Computer Modelling and Simulation of AGVs. Processes 8(12), 1648, 1-15. https://www.doi.org/10.3390/pr8121648
- 5. Golińska-Dawson P., Werner-Lewandowska K., Kosacka-Olejnik M., 2021, Responsible Resource Management in Remanufacturing - Framework for Qualitative Assessment in Small and Medium-Sized Enterprises. Resources 10(2), 19, 1-17. https://www.doi.org/10.3390/resources10020019
- 6. Gołda G., Kampa A., Foit K., 2018, Study of Inter-Operational Breaks Impact on Materials Flow in Flexible Manufacturing System. IOP Conference Series: Materials Science and Engineering 400, 1-6. https://www.doi.org/10.1088/1757-899X/400/2/022030
- 7. Gunawan A., Widjaja A.T., Vansteenwegen P., Yu V.F., 2021, A Matheuristic Algorithm for the Vehicle Routing Problem with Cross-Docking. Applied Soft Computing 103, 107163, 1-13. https://www.doi.org/10.1016/j.asoc.2021.107163
- 8. Guner H.U., Chinnam R.B., Murat A., 2016, Simulation Platform for Anticipative Plant-Level Maintenance Decision Support System. International Journal of Production Research 54(6), 1785–1803. https://www.doi.org/10.1080/00207543.2015.1064179
- 9. Gwiazda A., Monica Z., Ćwikła G., Grabowik C., Kalinowski K., 2017, Analiza czasów obróbki kół zębatych jako element oceny produktywności i szacowania kosztów wytwarzania [Analysis of Gear Machining Times as Part of Productivity Assessment and Manufacturing Cost Estimation], in Knosala R. (red.): Innowacje w zarządzaniu i inżynierii produkcji, Oficyna Wydaw. PTZP, Opole, 424-437, Available from Internet: http://www.ptzp.org.pl/files/konferencje/kzz/artyk_pdf_2017/T1/t1_424.pdf
- 10. Jaiswal Ch., 2023, Gear Motor Market Research Report. Market Forecast Til 2030, MRFR Publishing New York, Available from Internet: https://www.marketresearchfuture.com/reports/gear-motor-market-7473
- 11. Kampa A., 2023, Modeling and Simulation of a Digital Twin of a Production System for Industry 4.0 with Work-in-Process Synchronization. Appl. Sci. 13(22), 12261, 1-18. https://www.doi.org/10.3390/app132212261
- 12. Kampa A., Paprocka I., 2021, Analysis of Energy Efficient Scheduling of the Manufacturing Line with Finite Buffer Capacity and Machine Setup and Shutdown Times. Energies 14(21), 7446, 1-25. https://www.doi.org/10.3390/en14217446
- 13. Krenczyk D., Paprocka I., 2023, Integration of Discrete Simulation, Prediction, and Optimization Methods for a Production Line Digital Twin Design. Materials 16(6), 2339, 1-21. https://www.doi.org/10.3390/ma16062339
- 14. Liebherr, Gear Technology and Automation Systems. 2023. Available from Internet: https://www.liebherr.com/en/usa/products/gear-technology-and-automation-systems/gear-technology-and-automation-systems.html
- 15. Mešić A., Miškić S., Stević,Ž., Mastilo Z., 2022, Hybrid MCDM Solutions for Evaluation of the Logistics Performance Index of the Western Balkan Countries. Economics, 10(1) 13-34. https://doi.org/10.2478/eoik-2022-0004
- 16.Michlowicz E., 2022, Assessment of the Modernized Production System Through Selected TPM Method Indicators. Maintenance and Reliability, 24(4), 677–686. https://www.doi.org/10.17531/ein.2022.4.8
- 17. Nieoczym A., Tarkowski S., 2011, The Modeling of the Assembly Line with a Technological Automated Guided Vehicle (AGV). LogForum 7(3), 4, 35-42. http://www.logforum.net/vol7/issue3/no4
- 18. Olender-Skóra M., 2021, Application of Digital Twins in Specific Manufacturing Processes. IOP Conference Series: Materials Science and Engineering, 1182, 1-6. https://www.doi.org/10.1088/1757-899X/1182/1/012058
- 19. Olender-Skóra M., Banaś W., 2022, Application of a Digital Twin for Manufacturing Process Simulation. Journal of Physics - Conference Series, 2198, 1-9. https://www.doi.org/10.1088/1742-6596/2198/1/012029
- 20. Olender-Skóra M., Gonet A., 2022, Application of Scheduling Systems in Smart Manufacturing. Journal of Physics - Conference Series, 2198, 1-11. https://www.doi.org/10.1088/1742-6596/2198/1/012012
- 21. Paprocka I., Skołud B., 2022, A Predictive Approach for Disassembly Line Balancing Problems. Sensors 22(10), 3920, 1-19. https://www.doi.org/10.3390/s22103920
- 22. Pawlewski P, Kosacka-Olejnik M, Werner-Lewandowska K., 2021, Digital Twin Lean Intralogistics: Research Implications. Applied Sciences. 11(4):1495. https://doi.org/10.3390/app11041495
- 23. Sénquiz-Díaz C., 2021, Transport infrastructure quality and logistics performance in exports, Economics, Vol. 9 (1), 107-124. https://www.doi.org/10.2478/eoik-2021-0008
- 24. Sharma M., Luthra S., Joshi S., Kumar A., Jain A., 2023, Green Logistics Driven Circular Practices Adoption in Industry 4.0 Era: A Moderating Effect of Institution Pressure and Supply Chain Flexibility. Journal of Cleaner Production 383, 135284. https://www.doi.org/10.1016/j.jclepro.2022.135284
- 25. Simic V., Dabic-Miletic S., Babaee Tirkolaee E., Stević Ž., Ala A., Amirteimoori A., 2023, Neutrosophic LOPCOW-ARAS Model for Prioritizing Industry 4.0-Based Material Handling Technologies in Smart and Sustainable Warehouse Management Systems. Applied Soft Computing 143, 110400. https://www.doi.org/10.1016/j.asoc.2023.110400
- 26. Szwarc E., Bocewicz G., Golińska-Dawson P., Banaszak Z., 2023, Proactive Operations Management: Staff Allocation with Competence Maintenance Constraints. Sustainability 15(3), 1949, 1-20. https://www.doi.org/10.3390/su15031949
- 27. Tubis A., Rohman J., 2023, Intelligent Warehouse in Industry 4.0 - Systematic Literature Review. Sensors 23(8), 4105, 1-28. https://www.doi.org/10.3390/s23084105
- 28. Żuchowski W., 2022, The Smart Warehouse Trend: Actual Level of Technology Availability. LogForum 18(2), 227-235. https://www.doi.org/10.17270/J.LOG.2021.702
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-2663a5fd-1ccd-4e90-9a4c-f9248791a6be
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