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Tytuł artykułu

Management optimizing the costs and duration time of the process in the production system

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This article proposes a method to support decision making from a cost management perspective in the initial tage of production planning. In a research analyzed the problem of selecting production re-sources for order realization. The research was based on computer simulation. The developed model focuses on the planning of the production process in the event that the products have not yet been produced and it is necessary to decide where to produce it (with what production resources) so that the total production costs are as low as possible. In this concept, the FlexSim simulation environment with a built-in optimization module was used to solve the problem. The basic steps of simulation model built were discussed, taking into account the necessary information and input data. The results show the impact of the application of selected simulation scenarios on the level of use of production re-sources, due to the minimization of the total production costs and the duration time of the production process.
Rocznik
Strony
163--170
Opis fizyczny
Bibliogr. 38 poz.
Twórcy
autor
  • Czestochowa University of Technology, ul. Dąbrowskiego 69, 42-201 Czestochowa, Poland
Bibliografia
  • 1. Beaverstock, M., Greenwood, A.G., Lavery, E., Nordgren, B., 2012. Applied Simulation: Modeling and Analysis Using FlexSim. FlexSim Software Products.
  • 2. Drbúl, M., Stančeková, D., Babík, O., Holubjak, J., Görögová, I., Varga, D., 2016. Simulation Possibilities of 3D Measuring in Progressive Control of Production. Manufacturing Technology, 16(1), 53–58. DOI: 10.21062/ujep/x.2016/a/1213-2489/MT/16/1/53.
  • 3. FlexSim: User manual, 2017.
  • 4. Garrido, J.M., 2009. Object Oriented Simulation: A Modeling and Programming Perspective. Dordrecht: Springer US.
  • 5. Ingaldi, M., 2020. A new approach to quality management: conceptual matrix of service attributes. Polish Journal of Management Studies, 22(2), 187–200. DOI: 10.17512/pjms.2020.22.2.13.
  • 6. Jelonek, D., Mesjasz-Lech, A., Stępniak, C., Turek, T., Ziora, L., 2020. The Artificial Intelligence Application in the Management of Contemporary Organization: Theoretical Assumptions, Current Practices and Research Review. W: K. Arai & R. Bhatia (red.), Lecture Notes in Networks and Systems: volume 69-70. Advances in Information and Communication. Proceedings of the 2019 Future of Information and Communication Conference (FICC) (nr. 69, s. 319–327). Cham: Springer. DOI: 10.1007/978-3-030-12388-8_23.
  • 7. Jędrzejczyk, Z., Kukuła, K., Skrzypek, J., Walkosz, A., 2020. Badania operacyjne w przykładach i zadaniach (Wydanie siódme, zmienione). Warszawa: Wydawnictwo Naukowe PWN.
  • 8. Kaczmar, I., 2015. Cost optimization of blend preparation with the use of the flexsim environment. Agricultural Engineering, 4(156), 51–60. DOI: 10.14654/ir.2015.156.151.
  • 9. Kaczmar, I., 2016. The use of simulation and optimization in managing the manufacturing process — case study. Gospodarka Materiałowa i Logistyka, 2016(4), 21–28.
  • 10. Kaczmar, I., 2019. Komputerowe modelowanie i symulacje procesów logistycznych w środowisku flexsim. Wydawnictwo Naukowe PWN.
  • 11. Karcz, J., Ślusarczyk, B., 2021. Criteria of quality requirements deciding on choice of the logistic operator from a perspective of his customer and the end recipient of goods. Production Engineering Archives, 27(1), 58–68. DOI: 10.30657/pea.2021.27.8.
  • 12. Klimecka-Tatar, D., 2018. Context of production engineering in management model of Value Stream Flow according to manufacturing industry. Production Engineering Archives, 21(21), 32–35. DOI: 10.30657/pea.2018.21.07.
  • 13. Klimecka-Tatar, D., Ingaldi, M., 2020. Assessment of the Technological Position of a Selected Enterprise in the Metallurgical Industry. Materials Research Proceedings, 2020(17), 72–78. DOI: 10.21741/9781644901038-11.
  • 14. Klimecka-Tatar, D., Ingaldi, M., Obrecht, M., 2021. Sustainable Developement in Logistic – A Strategy for Management in Terms of Green Transport. Management Systems in Production Engineering, 29(2), 91–96. DOI: 10.2478/mspe-2021-0012.
  • 15. Knop, K., 2019. Evaluation of quality of services provided by transport & logistics operator from pharmaceutical industry for improvement purposes. Transportation Research Procedia, 40, 1080–1087. DOI: 10.1016/j.trpro.2019.07.151.
  • 16. Knop, K., 2020a. Importance of visual management in metal and automotive branch and its influence in building a competitive advantage. Polish Journal of Management Studies, 22(1), 263–278. DOI: 10.17512/pjms.2020.22.1.17.
  • 17. Knop, K., 2020b. Indicating and analysis the interrelation between terms – visual: management, control, inspection and testing. Production Engineering Archives, 26(3), 110–120. DOI: 10.30657/pea.2020.26.22.
  • 18. Kolda, T.G., Lewis, R.M., Torczon, V., 2003. Optimization by Direct Search: New Perspectives on Some Classical and Modern Methods. SIAM Review, 45(3), 385–482. DOI: 10.1137/S003614450242889.
  • 19. Krenczyk, D., Kempa, W.M., Kalinowski, K., Grabowik, C., Paprocka, I., 2017. Production planning and scheduling with material handling using modelling and simulation. MATEC Web of Conferences, 112, 9015. DOI: 10.1051/matecconf/201711209015.
  • 20. Krynke, M., Mielczarek, K., 2018. Applications of linear programming to optimize the cost-benefit criterion in production processes. MATEC Web of Conferences, 183, 4004. DOI: 10.1051/matecconf/201818304004.
  • 21. Krynke, M., Mielczarek, K., Vaško, A., 2019. Analysis of the Problem of Staff Allocation to Work Stations. Quality Production Improvement - QPI, 1(1), 545–550. DOI: 10.2478/cqpi-2019-0073.
  • 22. Krynke, M., Mielczarek, K., Kiriliuk, O., 2021. Cost Optimization and Risk Minimization During Teamwork Organization. Management Systems in Production Engineering, 29(2), 145–150. DOI: 10.2478/mspe-2021-0019.
  • 23. Krynke, M., 2020. Risk Management in the Process of Personnel Allocation to Jobs. 8th International Conference System Safety: Human - Technical Facility - Environment (CzOTO 2019), 82–90.
  • 24. Kyncl, J., 2016. Digital Factory Simulation Tools. Manufacturing Technology, 16(2), 371–375. DOI: 10.21062/ujep/x.2016/a/1213-2489/MT/16/2/371.
  • 25. Kyncl, J., Kellner, T., Kubiš, R., 2017. Tricanter Production Process Optimization by Digital Factory Simulation Tools. Manufacturing Technology, 17(1), 49–53. DOI: 10.21062/ujep/x.2017/a/1213-2489/MT/17/1/49.
  • 26. Le, T.D.C., Nguyen, D.D., Oláh, J., Pakurár, M., 2020. Optimal vehicle route schedules in picking up and delivering cargo containers considering time windows in logistics distribution networks: A case study. Production Engineering Archives, 26(4), 174–184. DOI: 10.30657/pea.2020.26.31.
  • 27. Matuszny, M., 2020. Building decision trees based on production knowledge as support in decision-making process. Production Engineering Archives, 26(2), 36–40. DOI: 10.30657/pea.2020.26.08.
  • 28. Mazur, M., Momeni, H., 2019. LEAN Production issues in the organization of the company - results. Production Engineering Archives, 22(22), 50–53. DOI: 10.30657/pea.2019.22.10.
  • 29. Niciejewska, M., Idzikowski, A., Škurková, K.L., 2021. Impact of Technical, Organizational and Human Factors on Accident Rate of Small-Sized Enterprises. Management Systems in Production Engineering, 29(2), 139–144. DOI: 10.2478/mspe-2021-0018.
  • 30. Pietraszek, J., Radek, N., Goroshko, A.V., 2020. Challenges for the DOE methodology related to the introduction of Industry 4.0. Production Engineering Archives, 26(4), 190–194. DOI: 10.30657/pea.2020.26.33.
  • 31. Setamanit, S., 2019. Improving transportation contract management using simulation. Polish Journal of Management Studies, 20(2), 466–477. DOI: 10.17512/pjms.2019.20.2.39.
  • 32. Staniszewska, E., Klimecka-Tatar, D., Obrecht, M., 2020. Eco-design processes in the automotive industry. Production Engineering Archives, 26(4), 131–137. DOI: 10.30657/pea.2020.26.25.
  • 33. Sujová, E., Střihavková, E., Čierna, H., 2018. An Analysis of the Assembly Line Modernization by Using Simulation Software. Manufacturing Technology, 18(5), 839–845. DOI: 10.21062/ujep/187.2018/a/1213-2489/MT/18/5/839.
  • 34. Sujová, E., Vysloužilová, D., Čierna, H., Bambura, R., 2020. Simulation Models of Production Plants as a Tool for Implementation of the Digital Twin Concept into Production. Manufacturing Technology, 20(4), 527–533. DOI: 10.21062/mft.2020.064.
  • 35. Ulewicz, R., 2014. Practical Application of Quality Tools in the Cast Iron Foundry. Manufacturing Technology, 14(1), 104–111. DOI: 10.21062/ujep/x.2014/a/1213-2489/MT/14/1/104.
  • 36. Ulewicz, R., Blaskova, M., 2018. Sustainable development and knowledge management from the stakeholders’ point of view. Polish Journal of Management Studies, 18(2), 363–374. DOI: 10.17512/pjms.2018.18.2.29.
  • 37. Ulewicz, R. Kleszcz, D. Ulewicz M. 2021. Management Systems in Production Engineering, 29(3), 203-207. DOI: 10.2478/mspe-2021-0025
  • 38. Zhuang, C., Liu, J., Xiong, H., 2018. Digital twin-based smart production management and control framework for the complex product assembly shop-floor. Journal of Advanced Manufacturing Technology. (96), 1149–1163. DOI: 10.1007/s00170-018-1617-6.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-bbf44955-cd18-4c36-95c5-7ab6a93c54f4
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