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Optimization of Welding Pliers Production for the Automotive Industry – Case Study

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Języki publikacji
EN
Abstrakty
EN
The paper presents a production process optimization by the simulation approach. The research was realized by a case study for an enterprise producing welding pliers for the automotive industry. The solution of the study was realized in the environment of the Tecnomatix Plant Simulation software, which can simulate the production process after choosing the right conditions and connections among the elements. The research aimed at creating a simulation model of the evaluated process with real data and designing optimization solution of the production process, which assumes an increase in the devices and machines utilization. The results showed improvements in the efficiency of the production process, in the particular case increasing by more than 25%.
Twórcy
  • Technical University of Kosice, Faculty of Mining, Ecology, Process Control and Geotechnologies, Institute of Logistics and Transport, Letná 9, 040 01 Kosice, Slovakia
  • Technical University of Kosice, Faculty of Mining, Ecology, Process Control and Geotechnologies, Institute of Logistics and Transport, Letná 9, 040 01 Kosice, Slovakia
  • Institute of Technology and Business in České Budějovice, Faculty of Technology, Department of Transport and Logistics, Okružní 10, 370 01 České Budějovice, Czech Republic
Bibliografia
  • 1. Fowler J.W. and Rose O. Grand Challenges in Modeling and Simulation of Complex Manufacturing Systems. Simulation: the Society for Modeling and Simulation International 80, 2004, 469–476.
  • 2. Bucki R. and Chramcov, B. Modelling and simulation of the order realization in the serial production system. International Journal of Mathematical Models and Methods in Applied Sciences 5(7), 2011, 1233–1240.
  • 3. Schindlerova V. and Šajdlerova I. Use of the Dynamic Simulation to Reduce Handling Complexity in the Manufacturing Process. Advances in Science and Technology Research Journal 14, 2020, 81–88.
  • 4. Akberdin A.A., Kim A.S. and Sultangaziev R.B. Experiment Planning in the Simulation of Industrial Processes. Steel Transl. 48, 2018, 573–577.
  • 5. Bergmann S. and Strassburger S. Challenges for the Automatic Generation of Simulation Models for Production Systems. Proceedings of Summer Simulation Multiconference – SummerSim ‘10, 2010, 545–549.
  • 6. Puntel-Schmidt P. and Fay A. Levels of Detail and Appropriate Model Types for Virtual Commissioning in Manufacturing Engineering. IFAC – PapersOnLine 48(1), 2015, 922v927.
  • 7. Savsar M. and Al-Jawini A. Simulation analysis of just-in-time production systems International Journal of Production Economics 42, 1995, 67–78.
  • 8. Chramcov B., Beran P., Daníček L. and Jašek R. A simulation approach to achieving more efficient production systems. International Journal of Mathematics and Computers in Simulation 5, 2011, 299–309.
  • 9. Bakoa B. and Božek P. Trends in Simulation and Planning of Manufacturing Companies Procedia Engineering 149, 2016, 571–575.
  • 10. Lim L.L., Alpan G. and Penz B. A simulation-optimization approach for sales and operations planning in build-to-order industries with distant sourcing: Focus on the automotive industry. Computers & Industrial Engineering 112, 2017, 469–482.
  • 11. Fabianova J., Kacmary P. and Janekova J. Operative production planning utilising quantitative forecasting and Monte Carlo simulations. Open Eng. 9, 2019, 613–622.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-3ca6b6da-cbf7-4983-b240-a62f087db931
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