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

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

Management Systems in Production Engineering
Volume 32 (2024): Issue 3 (August 2024)
By:
Open Access
|Sep 2024

References

  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: <a href="https://doi.org/10.24425/ace.2020.134410." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.24425/ace.2020.134410.</a>
    Search in Google ScholarBack to article
  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: <a href="https://doi.org/10.4028/www.scientific.net/AMM.510.265." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.4028/www.scientific.net/AMM.510.265.</a>
    Search in Google ScholarBack to article
  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.
    Search in Google ScholarBack to article
  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.
    Search in Google ScholarBack to article
  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.
    Search in Google ScholarBack to article
  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: <a href="https://doi.org/10.1007/s40092-017-0201-2." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1007/s40092-017-0201-2.</a>
    Search in Google ScholarBack to article
  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: <a href="https://doi.org/10.3390/en16217429." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.3390/en16217429.</a>
    Search in Google ScholarBack to article
  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: <a href="https://doi.org/10.3311/PPtr.23546." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.3311/PPtr.23546.</a>
    Search in Google ScholarBack to article
  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: <a href="https://doi.org/10.17512/pjms.2022.26.2.20." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.17512/pjms.2022.26.2.20.</a>
    Search in Google ScholarBack to article
  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: <a href="https://doi.org/10.17512/bozpe.2022.11.05." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.17512/bozpe.2022.11.05.</a>
    Search in Google ScholarBack to article
  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: <a href="https://doi.org/10.2478/mspe-2021-0019." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.2478/mspe-2021-0019.</a>
    Search in Google ScholarBack to article
  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: <a href="https://doi.org/10.30657/pea.2021.27.21." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.30657/pea.2021.27.21.</a>
    Search in Google ScholarBack to article
  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: <a href="https://doi.org/10.17512/pjms.2022.26.2.07." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.17512/pjms.2022.26.2.07.</a>
    Search in Google ScholarBack to article
  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
    Search in Google ScholarBack to article
  15. I. Kaczmar, Komputerowe modelowanie i symulacje procesów logistycznych w środowisku FlexSim. Warszawa: Wydawnictwo Naukowe PWN, 2019.
    Search in Google ScholarBack to article
  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.
    Search in Google ScholarBack to article
  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: <a href="https://doi.org/10.1515/mspe-2019-0021." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1515/mspe-2019-0021.</a>
    Search in Google ScholarBack to article
  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: <a href="https://doi.org/10.1051/matecconf/202134603105." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1051/matecconf/202134603105.</a>
    Search in Google ScholarBack to article
  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: <a href="https://doi.org/10.3390/en14113293." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.3390/en14113293.</a>
    Search in Google ScholarBack to article
  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: <a href="https://doi.org/10.1080/02827589309382802." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1080/02827589309382802.</a>
    Search in Google ScholarBack to article
  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: <a href="https://doi.org/10.30657/pea.2022.28.38." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.30657/pea.2022.28.38.</a>
    Search in Google ScholarBack to article
  22. M. Laguna, OptQuest, 2011. [Online]. Available: https://www.opttek.com/sites/default/files/pdfs/optquest-optimization%20of%20complex%20systems.pdf.
    Search in Google ScholarBack to article
  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: <a href="https://doi.org/10.1177/0037549718819804." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1177/0037549718819804.</a>
    Search in Google ScholarBack to article
  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: <a href="https://doi.org/10.24425/ace.2020.134410." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.24425/ace.2020.134410.</a>
    Search in Google ScholarBack to article
  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: <a href="https://doi.org/10.4028/www.scientific.net/AMM.510.265." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.4028/www.scientific.net/AMM.510.265.</a>
    Search in Google ScholarBack to article
  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.
    Search in Google ScholarBack to article
  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.
    Search in Google ScholarBack to article
  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.
    Search in Google ScholarBack to article
  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: <a href="https://doi.org/10.1007/s40092-017-0201-2." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1007/s40092-017-0201-2.</a>
    Search in Google ScholarBack to article
  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: <a href="https://doi.org/10.3390/en16217429." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.3390/en16217429.</a>
    Search in Google ScholarBack to article
  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: <a href="https://doi.org/10.3311/PPtr.23546." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.3311/PPtr.23546.</a>
    Search in Google ScholarBack to article
  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: <a href="https://doi.org/10.17512/pjms.2022.26.2.20." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.17512/pjms.2022.26.2.20.</a>
    Search in Google ScholarBack to article
  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: <a href="https://doi.org/10.17512/bozpe.2022.11.05." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.17512/bozpe.2022.11.05.</a>
    Search in Google ScholarBack to article
  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: <a href="https://doi.org/10.2478/mspe-2021-0019." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.2478/mspe-2021-0019.</a>
    Search in Google ScholarBack to article
  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: <a href="https://doi.org/10.30657/pea.2021.27.21." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.30657/pea.2021.27.21.</a>
    Search in Google ScholarBack to article
  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: <a href="https://doi.org/10.17512/pjms.2022.26.2.07." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.17512/pjms.2022.26.2.07.</a>
    Search in Google ScholarBack to article
  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
    Search in Google ScholarBack to article
  38. I. Kaczmar, Komputerowe modelowanie i symulacje procesów logistycznych w środowisku FlexSim. Warszawa: Wydawnictwo Naukowe PWN, 2019.
    Search in Google ScholarBack to article
  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.
    Search in Google ScholarBack to article
  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: <a href="https://doi.org/10.1515/mspe-2019-0021." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1515/mspe-2019-0021.</a>
    Search in Google ScholarBack to article
  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: <a href="https://doi.org/10.1051/matecconf/202134603105." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1051/matecconf/202134603105.</a>
    Search in Google ScholarBack to article
  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: <a href="https://doi.org/10.3390/en14113293." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.3390/en14113293.</a>
    Search in Google ScholarBack to article
  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: <a href="https://doi.org/10.1080/02827589309382802." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1080/02827589309382802.</a>
    Search in Google ScholarBack to article
  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: <a href="https://doi.org/10.30657/pea.2022.28.38." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.30657/pea.2022.28.38.</a>
    Search in Google ScholarBack to article
  45. M. Laguna, OptQuest, 2011. [Online]. Available: https://www.opttek.com/sites/default/files/pdfs/optquest-optimization%20of%20complex%20systems.pdf
    Search in Google ScholarBack to article
  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: <a href="https://doi.org/10.1177/0037549718819804." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1177/0037549718819804.</a>
    Search in Google ScholarBack to article
  47. FlexSim, User manual, 2017.
    Search in Google ScholarBack to article
DOI: https://doi.org/10.2478/mspe-2024-0032 | Journal eISSN: 2450-5781 | Journal ISSN: 2299-0461
Journal RSS Feed
Language: English
Page range: 339 - 344
Submitted on: Jan 1, 2024
Accepted on: Jul 1, 2024
Published on: Sep 5, 2024
Published by: STE Group sp. z.o.o.
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year
Keywords:
warehouse location optimization,
computer simulation,
FlexSim,
logistics
Related subjects:
Engineering,
Mechanical engineering,
Tools and methods,
Production technology,
Process engineering and industrial engineering,
Business and economics,
Business management,
Management accounting, financial controlling, cost calculation, investment

© 2024 Marek Krynke, published by STE Group sp. z.o.o.
This work is licensed under the Creative Commons Attribution 4.0 License.

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