Virtual simulation modeling as a key element of warehouse location optimization strategy

• Autorzy: Krynke Marek

Identyfikatory

DOI

10.2478/mspe-2024-0032

• Języki publikacji: EN

Abstrakty

EN

This article examines the utilization of computer simulation techniques for optimizing warehouse locations, an essential component of efficient supply chain management. The study employs a detailed simulation model built using FlexSim software to analyze various decision-making scenarios and identify the optimal warehouse locations while considering market demand for different products. The model integrates a finite set of decision variables and constraints specific to the logistics problem, offering a structured approach to evaluate alternative strategies. Key stages in the development of the simulation model are outlined, including the definition of input parameters, the execution of simulations, and the interpretation of results. The findings demonstrate that virtual simulation modeling significantly enhances decision-making processes by providing precise insights into the interactions within the distribution network. Additionally, the use of simulation results in considerable time and cost savings by reducing the need for costly physical trials. This research underscores the effectiveness of computer simulation in optimizing warehouse locations, contributing to improved supply chain performance and operational efficiency.

Słowa kluczowe

EN

warehouse location optimization; computer simulation; FlexSim; logistics

• Wydawca: STE GROUP
• Czasopismo: Management Systems in Production Engineering
• Rocznik: 2024
• Tom: nr 3 (32)
• Strony: 339--344
• Opis fizyczny: Bibliogr. 47 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. Izdebski, I. Jacyna-Gołda, P. Gołębiowski, and J. Plandor, “The Optimization Tool Supporting Supply Chain Management in the Multi-Criteria Approach,” Archives of Civil Engineering, pp. 505-524, 2020, doi: 10.24425/ace.2020.134410.
• [2] X.L. Wang, M. Xu, J. Xiao, and R. Guo, “Optimization of Goods Locations Assignment of Automated Warehouse on Hierarchic Genetic Algorithm,” AMM, vol. 510, pp. 265-270, 2014, doi: 10.4028/www.scientific.net/AMM.510.265.
• [3] P. Pawlewski, M. Hoffmann, I. Kegel, K. Krawczyk, and A. Kołodziej, „Proces referencyjny jako narzędzie przyspie-szające modelowanie symulacyjne procesów logistycznych,” Zeszyty Naukowe Politechniki Poznańskiej. Organizacja i Zarządzanie 85, 2022.
• [4] M. Beaverstock, A. Greenwood, and W. Nordgren, Applied simulation: modeling and analysis using FlexSim, 5th ed.: Published by FlexSim Software Products, Inc., Canyon Park Technology Center, Building A Suite 2300, Orem, UT 84097 USA., 2017.
• [5] J. Dorismond, “Supermarket optimization: Simulation modeling and analysis of a grocery store layout,” in 2016 Winter Simulation Conference (WSC), Washington, DC, USA, Dec. 2016, pp. 3656-3657.
• [6] N. Chiadamrong and V. Piyathanavong, “Optimal design of supply chain network under uncertainty environment using hybrid analytical and simulation modeling approach,” J Ind Eng Int, vol. 13, no. 4, pp. 465-478, 2017, doi: 10.1007/s40092-017-0201-2.
• [7] S. Kim, Y. Choi, and S. Kim, “Simulation Modeling in Supply Chain Management Research of Ethanol: A Review,” Energies, vol. 16, no. 21, p. 7429, 2023, doi: 10.3390/en16217429.
• [8] M. Krynke and M. Mazur, “Innovative Work Order Planning with Process Optimization Using Computer Simulation in the Automotive Industry, in the Case of Repair Workshops,” Period. Polytech. Transp. Eng., 2020, doi: 10.3311/PPtr.23546.
• [9] 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.
• [10] M.O. Mohammadi, T. Dede, and M. Grzywiński, “Solving a stochastic time-cost-quality trade-off problem by meta-heuristic optimization algorithms,” BoZPE, vol. 11, no. 2022.11, pp. 41-48, 2022, doi: 10.17512/bozpe.2022.11.05.
• [11] 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.
• [12] 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.
• [13] 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.
• [14] M. Odlanicka-Poczobutt, „Lokalizacja własnych punktów dystrybucji metodą środka ciężkości na przykładzie wybra-nego producenta produktów drewnopochodnych,” Zeszyty Naukowe Politechniki Śląskiej. Organizacja i Zarządzanie, no. 78, pp. 335-351, 2015. [Online]. Available: http://www.woiz.polsl.pl/znwoiz/z78/Odlanicka-Poczo-butt.pdf.
• [15] I. Kaczmar, Komputerowe modelowanie i symulacje procesów logistycznych w środowisku FlexSim. Warszawa: Wydawnictwo Naukowe PWN, 2019.
• [16] E. Kuczyńska and J. Ziółkowski, „Wyznaczanie lokalizacji obiektu logistycznego z zastosowaniem metody wyważo-nego środka ciężkości – studium przypadku,” Biuletyn WAT, vol. 61, no. 3, pp. 339-351, 2012.
• [17] S. Supsomboon, “Simulation for Jewelry Production Process Improvement Using Line Balancing: A Case Study,” Management Systems in Production Engineering, vol. 27, no. 3, pp. 127-137, 2019, doi: 10.1515/mspe-2019-0021.
• [18] 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.
• [19] 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.
• [20] 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.
• [21] 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.
• [22] M. Laguna, OptQuest, 2011. [Online]. Available: https://www.opttek.com/sites/default/files/pdfs/optquest-optimization%20of%20complex%20systems.pdf.
• [23] 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.
• [24] FlexSim, User manual, 2017. M. Izdebski, I. Jacyna-Gołda, P. Gołębiowski, and J. Plandor, “The Optimization Tool Supporting Supply Chain Management in the Multi-Criteria Approach,” Archives of Civil Engineering, pp. 505-524, 2020, doi: 10.24425/ace.2020.134410.
• [25] X.L. Wang, M. Xu, J. Xiao, and R. Guo, “Optimization of Goods Locations Assignment of Automated Warehouse on Hierarchic Genetic Algorithm,” AMM, vol. 510, pp. 265-270, 2014, doi: 10.4028/www.scientific.net/AMM.510.265.
• [26] P. Pawlewski, M. Hoffmann, I. Kegel, K. Krawczyk, and A. Kołodziej, „Proces referencyjny jako narzędzie przyspie-szające modelowanie symulacyjne procesów logistycznych,” Zeszyty Naukowe Politechniki Poznańskiej. Organizacja i Zarządzanie 85, 2022.
• [27] M. Beaverstock, A. Greenwood, and W. Nordgren, Applied simulation: modeling and analysis using FlexSim, 5th ed.: Published by FlexSim Software Products, Inc., Canyon Park Technology Center, Building A Suite 2300, Orem, UT 84097 USA., 2017.
• [28] J. Dorismond, “Supermarket optimization: Simulation modeling and analysis of a grocery store layout,” in 2016 Winter Simulation Conference (WSC), Washington, DC, USA, Dec. 2016, pp. 3656-3657.
• [29] N. Chiadamrong and V. Piyathanavong, “Optimal design of supply chain network under uncertainty environment using hybrid analytical and simulation modeling approach,” J Ind Eng Int, vol. 13, no. 4, pp. 465-478, 2017, doi: 10.1007/s40092-017-0201-2.
• [30] S. Kim, Y. Choi, and S. Kim, “Simulation Modeling in Supply Chain Management Research of Ethanol: A Review,” Energies, vol. 16, no. 21, p. 7429, 2023, doi: 10.3390/en16217429.
• [31] M. Krynke and M. Mazur, “Innovative Work Order Planning with Process Optimization Using Computer Simulation in the Automotive Industry, in the Case of Repair Workshops,” Period. Polytech. Transp. Eng., 2020, doi: 10.3311/PPtr.23546.
• [32] 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.
• [33] M.O. Mohammadi, T. Dede, and M. Grzywiński, “Solving a stochastic time-cost-quality trade-off problem by meta-heuristic optimization algorithms,” BoZPE, vol. 11, no. 2022.11, pp. 41-48, 2022, doi: 10.17512/bozpe.2022.11.05.
• [34] 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.
• [35] 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.
• [36] 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.
• [37] M. Odlanicka-Poczobutt, „Lokalizacja własnych punktów dystrybucji metodą środka ciężkości na przykładzie wybra-nego producenta produktów drewnopochodnych,” Zeszyty Naukowe Politechniki Śląskiej. Organizacja i Zarządzanie, no. 78, pp. 335-351, 2015. [Online]. Available: http://www.woiz.polsl.pl/znwoiz/z78/Odlanicka-Poczo-butt.pdf.
• [38] I. Kaczmar, Komputerowe modelowanie i symulacje procesów logistycznych w środowisku FlexSim. Warszawa: Wydawnictwo Naukowe PWN, 2019.
• [39] E. Kuczyńska and J. Ziółkowski, „Wyznaczanie lokalizacji obiektu logistycznego z zastosowaniem metody wyważo-nego środka ciężkości – studium przypadku,” Biuletyn WAT, vol. 61, no. 3, pp. 339-351, 2012.
• [40] S. Supsomboon, “Simulation for Jewelry Production Process Improvement Using Line Balancing: A Case Study,” Management Systems in Production Engineering, vol. 27, no. 3, pp. 127-137, 2019, doi: 10.1515/mspe-2019-0021.
• [41] 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.
• [42] 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.
• [43] 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.
• [44] 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.
• [45] M. Laguna, OptQuest, 2011. [Online]. Available: https://www.opttek.com/sites/default/files/pdfs/optquest-optimization%20of%20complex%20systems.pdf.
• [46] 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.
• [47] FlexSim, User manual, 2017.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki i promocja sportu (2025).