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Virtual Simulation Modeling as a Key Element of Warehouse Location Optimization Strategy

  
05. Sept. 2024

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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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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 Search in Google Scholar

I. Kaczmar, Komputerowe modelowanie i symulacje procesów logistycznych w środowisku FlexSim. Warszawa: Wydawnictwo Naukowe PWN, 2019. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

M. Laguna, OptQuest, 2011. [Online]. Available: https://www.opttek.com/sites/default/files/pdfs/optquest-optimization%20of%20complex%20systems.pdf. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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 Search in Google Scholar

I. Kaczmar, Komputerowe modelowanie i symulacje procesów logistycznych w środowisku FlexSim. Warszawa: Wydawnictwo Naukowe PWN, 2019. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

M. Laguna, OptQuest, 2011. [Online]. Available: https://www.opttek.com/sites/default/files/pdfs/optquest-optimization%20of%20complex%20systems.pdf Search in Google Scholar

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. Search in Google Scholar

FlexSim, User manual, 2017. Search in Google Scholar