Analysis of the impact of effective time management on workstation efficiency using a multi-criteria optimization approach

• Autorzy: Krynke Marek

Identyfikatory

DOI

10.2478/mspe-2023-0034

• Języki publikacji: EN

Abstrakty

EN

The aim of the article is to analyze the impact of effective time management on the performance of workstations in the context of the conflict between maximizing workstation utilization and minimizing the number of items waiting in the queue. The article utilized the FlexSim program to build a simulation model of the workstation and conducted optimization using the built-in optimizer. The research demonstrated that effective time management has a positive impact on workstation performance by reducing the number of items waiting in the queue, leading to increased throughput and reduced delays in production processes. An important aspect of the analysis was the application of a multi-criteria optimization approach, which allowed for achieving a balance between maximizing workstation utilization and minimizing the number of items waiting. Multi-criteria optimization considers diverse goals and decision criteria, leading to a more comprehensive approach to optimizing production processes. As a result, effective time management on workstations, based on analysis and multi-criteria optimization, can significantly improve the efficiency and performance of production processes. This analysis can be a valuable tool for organizations seeking to optimize their processes and achieve a competitive advantage in the market. The analysis conducted in the article confirms that effective time management has a beneficial impact on workstation performance. The use of a multi-criteria approach in optimization enables achieving a balance between various decision factors. The presented simulation model and research results can be useful for decision-makers in the manufacturing field who aim to make more informed decisions regarding planning and optimizing production processes to enhance efficiency, effectiveness, and customer satisfaction.

Słowa kluczowe

EN

process management; simulation; FlexSim; optimization

• Wydawca: STE GROUP
• Czasopismo: Management Systems in Production Engineering
• Rocznik: 2023
• Tom: nr 3 (31)
• Strony: 306--311
• Opis fizyczny: Bibliogr. 30 poz., rys.

Twórcy

autor

Krynke Marek

• Czestochowa University of Technology ul. Dabrowskiego 69, 42-201 Czestochowa, Poland

• https://orcid.org/0000-0003-4417-1955

Bibliografia

• [1] M. Krynke, “Management optimizing the costs and duration time of the process in the production system,” Production Engineering Archives, vol. 27, no. 3, pp. 163-170, 2021, doi: 10.30657/pea.2021.27.21.
• [2] M. Krynke, “Personnel Management on the Production Line Using the FlexSim Simulation Environment,” Manufacturing Technology, vol. 21, no. 5, pp. 657-667, 2021, doi: 10.21062/mft.2021.073.
• [3] S. Luscinski and V. Ivanov, “Management and Production Engineering Review,” 2020.
• [4] M. Ingaldi and D. Klimecka-Tatar, “Digitization of the service provision process - requirements and readiness of the small and medium-sized enterprise sector,” Procedia Computer Science, vol. 200, pp. 237-246, 2022, doi: 10.1016/j.procs.2022.01.222.
• [5] R. Ulewicz and K. Mielczarek, “Machine operation efficiency in the production of car equipment,” in 13th International Scientific Conference 2022, p. 50070.
• [6] M. Beaverstock, A. Greenwood, and W. Nordgren, Applied simulation: modeling and analysis using FlexSim, 5 th ed.: Published by FlexSim Software Products, Inc., Canyon Park Technology Center, Building A Suite 2300, Orem, UT 84097 USA., 2017.
• [7] M. Laguna, OptQuest, 2011. [Online]. Available: https://www.opttek.com/sites/default/ files/pdfs/optquest-optimization%20of%20complex%20systems.pdf
• [8] M. Frantzén, A. H. Ng, and P. Moore, “A simulation-based scheduling system for real-time optimization and decision making support,” Robotics and Computer-Integrated Manufacturing, vol. 27, no. 4, pp. 696-705, 2011, doi: 10.1016/j.rcim.2010.12.006.
• [9] M. O. Mohammadi, T. Dede, and M. Grzywiński, “Solving a stochastic time-cost-quality trade-off problem by metaheuristic optimization algorithms,” BoZPE, vol. 11, 2022.11, pp. 41–48, 2022, doi: 10.17512/bozpe.2022.11.05.
• [10] Z. Čičková, M. Reiff, and P. Holzerová, “Applied multi-criteria model of game theory on spatial allocation problem with the influence of the regulator,” PJMS, vol. 26, no. 2, pp. 112-129, 2022, doi: 10.17512/pjms.2022.26.2.07.
• [11] . N. Ivanova, W. Biały, A. I. Korshunov, J. Jura, K. Kaczmarczyk, and K. Turczyński, “Increasing Energy Efficiency in Well Drilling,” Energies, vol. 15, no. 5, p. 1865, 2022, doi: 10.3390/en15051865.
• [12] D. Siwiec, A. Pacana, and R. Ulewicz, “Concept of a model to predict the qualitative-cost level considering customers’ expectations,” PJMS, vol. 26, no. 2, pp. 330-340, 2022, doi: 10.17512/pjms.2022.26.2.20.
• [13] O. Shatalova, E. Kasatkina, and V. Larionov, “Multi-criteria Optimization in Solving the Problem of Expanding Production Capacity of an Enterprise as a Method of Modeling Strategic Directions for the Development of Production Systems,” MATEC Web Conf., vol. 346, p. 3105, 2021, doi: 10.1051/matecconf/202134603105.
• [14] N. L. P. Hariastuti and Lukmandono, “A Review on Sustainable Value Creation Factors in Sustainable Manufacturing Systems,” Production Engineering Archives, vol. 28, no. 4, pp. 336-345, 2022, doi: 10.30657/pea.2022.28.42.
• [15] M. Krynke and D. Klimecka-Tatar, “Production costs management in process supported by external entities – Process flow optimization,” in 13th International Scientific Conference 2022, p. 50068.
• [16] S. M. Kalinović, D. I. Tanikić, J. M. Djoković, R. R. Nikolić, B. Hadzima, and R. Ulewicz, “Optimal Solution for an Energy Efficient Construction of a Ventilated Façade Obtained by a Genetic Algorithm,” Energies, vol. 14, no. 11, p. 3293, 2021, doi: 10.3390/en14113293.
• [17] V. V. Borisova, O. V. Demkina, A. V. Mikhailova, and R. Zieliński, “The enterprise management system: evaluating the use of information technology and information systems,” PJMS, vol. 20, no. 1, pp. 103-118, 2019, doi: 10.17512/pjms.2019.20.1.09.
• [18] J. April, F. Glover, J. P. Kelly, and M. Laguna, “Practical introduction to simulation optimization,” in Proceedings of the 2003 Winter Simulation Conference: Fairmont Hotel, New Orleans, LA, U.S.A., December 7-10, 2003, New Orleans, LA, USA, 2004, 2003, pp. 71–78.
• [19] C. Kardos, C. Laflamme, V. Gallina, and W. Sihn, “Dynamic scheduling in a job-shop production system with reinforcement learning,” Procedia CIRP, vol. 97, pp. 104–109, 2021, doi: 10.1016/j.procir.2020.05.210.
• [20] J. April, M. Better, F. Glover, J. Kelly, and M. Laguna, “Enhancing Business Process Management with Simulation Optimization,” in Proceedings of the 38th conference on Winter simulation, Monterey, CA, USA, Dec. 2006 - Dec. 2006, pp. 642-649.
• [21] F. P. Santos, Â. P. Teixeira and C. G. Soares., “Modeling, simulation and optimization of maintenance cost aspects on multi-unit systems by stochastic Petri nets with predicates,” SIMULATION, vol. 95, no. 5, pp. 461-478, 2018, doi: 10.1177/0037549718782655.
• [22] M. W. Sari, Herianto, I. B. Dharma, and A. E. Tontowi, “Integrated Production System on Social Manufacturing: A Simulation Study,” Management Systems in Production Engineering, vol. 30, no. 3, pp. 230-237, 2022, doi: 10.2478/mspe-2022-0029.
• [23] M. Krynke, K. Mielczarek, and O. Kiriliuk, “Cost Optimization and Risk Minimization During Teamwork Organization,” Management Systems in Production Engineering, vol. 29, no. 2, pp. 145-150, 2021, doi: 10.2478/mspe-2021- 0019.
• [24] V. A. Zherebko, O. A. Pisarenko, and V. P. Drabynko, “Simulation and genetic optimization of control systems by labview programming” Problems in programming, no. 2-3, pp. 288-295, 2018, doi: /10.15407/pp2018.02.288.
• [25] J. Tabor, “Ranking of management factors for safe maintenance system based on Grey Systems Theory,” Production Engineering Archives, vol. 27, no. 3, pp. 196-202, 2021, doi: 10.30657/pea.2021.27.26.
• [26] M. Daroń, “Simulations in planning logistics processes as a tool of decision-making in manufacturing companies,” Production Engineering Archives, vol. 28, no. 4, pp. 300- 308, 2022, doi: 10.30657/pea.2022.28.38.
• [27] M. Rostek, “Productivity and improvement of logistics processes in the company manufacturing vehicle semi-trailers – Case study,” Production Engineering Archives, vol. 28, no. 4, pp. 309-318, 2022, doi: 10.30657/pea.2022.28.39.
• [28] T. Pukkala and J. Kangas, “A heuristic optimization method for forest planning and decision making,” Scandinavian Journal of Forest Research, vol. 8, 1-4, pp. 560–570, 1993, doi: 10.1080/02827589309382802.
• [29] A. Jerbi, A. Ammar, M. Krid, and B. Salah, “Performance optimization of a flexible manufacturing system using simulation: the Taguchi method versus OptQuest,” Simulation, 2019, doi: 10.1177/0037549718819804.
• [30] FlexSim, User manual, 2017.

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).