Document Type : Research Paper


1 Department of Management Information Systems, Marmara University, Istanbul, Turkey.

2 Department of Logistics Management, Medipol University, Istanbul, Turkey.


In the decision theory, there are many useful tools for operations in logistics and Supply Chain Management (SCM). One of the vital trivets of logistics operations is warehouse management which is also one of the parts of a supply chain. Deciding on the location of a warehouse has a critical issue especially during an outbreak. In this study, we aimed that to figure out differences between the perceived importance of the considered criteria in the decision process regarding warehouse location in the medical sector in terms of the changing dynamics after the Covid-19 pandemic. Pursuing this goal, the results of a preliminary study which was resulted from the gathered data of a decision-making group including industry professionals before the pandemic outbreak were accepted as an anchor to obtain a comparison with the current state. To construct a proper representation of the post-Covid state, a similar methodology was used, and similar decision-makers data were collected with the preliminary study in the identification of the importance figures and causal relationships between criteria. According to comparative results of pre-and post-Covid studies, it is found that there are significant changes in the perceived role of adjacency to target markets and customs criteria in medical warehouse location decisions. It is obvious that the results will shed light on medical sector professionals’ decision process while adapting to the current pandemic conditions.


Main Subjects

  1. Chamola, V., Hassija, V., Gupta, V., & Guizani, M. (2020). A comprehensive review of the COVID-19 pandemic and the role of IoT, drones, AI, blockchain, and 5G in managing its impact. IEEE access8, 90225-90265. DOI:1109/ACCESS.2020.2992341
  2. Szczepański, E., Jachimowski, R., Izdebski, M., & Jacyna-Gołda, I. (2019). Warehouse location problem in supply chain designing: a simulation analysis. Archives of transport50(2), 101-110. DOI: 5604/01.3001.0013.5752
  3. Guerin, P. J., Singh-Phulgenda, S., & Strub-Wourgaft, N. (2020). The consequence of COVID-19 on the global supply of medical products: why indian generics matter for the world?. F1000Research9(225). DOI: 12688/f1000research.23057.1
  4. Tuncalı Yaman, T., & Akkartal, G. R. (2020). Warehouse location selection decision systems for medical sector. Proceedings of the 2020 fourth world conference on smart trends in systems, security and sustainability (WorldS4), (pp. 208-213). IEEE.
  5. Abdullah, L., & Goh, P. (2019). Decision making method based on Pythagorean fuzzy sets and its application to solid waste management. Complex & intelligent systems5(2), 185-198.
  6. Senvar, O., Tuzkaya, U. R., & Kahraman, C. (2014). Supply chain performance measurement: an integrated DEMATEL and Fuzzy-ANP approach. In Supply chain management under fuzziness(pp. 143-165). Springer, Berlin, Heidelberg.
  7. Peng, X., & Selvachandran, G. (2019). Pythagorean fuzzy set: state of the art and future directions. Artificial intelligence review52(3), 1873-1927.
  8. Bolturk, E. (2018). Pythagorean fuzzy CODAS and its application to supplier selection in a manufacturing firm. Journal of enterprise information management, 31(4), 550-564.
  9. Mishra, N., Kumar, V., Kumar, N., Kumar, M., & Tiwari, M. K. (2011). Addressing lot sizing and warehousing scheduling problem in manufacturing environment. Expert systems with applications38(9), 11751-11762.
  10. Li, Q., Liu, Q. Q., Tang, C. F., Li, Z. W., Wei, S. C., Peng, X. R., ... & Yang, Q. (2020, June). Warehouse Vis: a visual analytics approach to facilitating warehouse location selection for business districts. Computer graphics forum(Vol. 39, No. 3, pp. 483-495).
  11. Ashrafzadeh, M., Rafiei, F. M., Isfahani, N. M., & Zare, Z. (2012). Application of fuzzy TOPSIS method for the selection of Warehouse Location: a case study. Interdisciplinary journal of contemporary research in business3(9), 655-671.
  12. Ansari, M., & Smith, J. S. (2020, December). Gravity clustering: a correlated storage location assignment problem approach. 2020 winter simulation conference (WSC)(pp. 1288-1299). IEEE. DOI: 1109/WSC48552.2020.9384029
  13. Kostikov, E., Jílkova, P., & Kotatkova Stranska, P. (2021). Optimization of e-commerce distribution center location. Marketing and management of innovations, 5(2), 166-178.
  14. TAŞ, M. A. (2021). Assessment of site selection criteria for medical waste during COVID-19 pandemic. Avrupa bilim ve teknoloji dergisi, (28), 63-69.
  15. Arslan, M. (2020). Application of AHP method for the selection of pharmaceutical warehouse location. Journal of faculty of pharmacy of ankara university44(2), 253-264.
  16. Kamba, P. F., Ireeta, M. E., Balikuna, S., & Kaggwa, B. (2017). Threats posed by stockpiles of expired pharmaceuticals in low-and middle-income countries: a Ugandan perspective. Bulletin of the world health organization95(8), 594-598.
  17. Gergin, R. E., & Peker, İ. (2019). Literature review on success factors and methods used in warehouse location selection. Pamukkale üniversitesi mühendislik bilimleri dergisi25(9), 1062-1070.
  18. Khan, S. I., & Hoque, A. S. M. L. (2015, December). Development of national health data warehouse Bangladesh: privacy issues and a practical solution. 2015 18th international conference on computer and information technology (ICCIT)(pp. 373-378). IEEE. DOI: 1109/ICCITechn.2015.7488099
  19. Aghezzaf, E. H. (2007). Production planning and warehouse management in supply networks with inter-facility mold transfers. European journal of operational research182(3), 1122-1139.
  20. Khan, S., Haleem, A., Deshmukh, S. G., & Javaid, M. (2021). Exploring the impact of COVID-19 pandemic on medical supply chain disruption. Journal of industrial integration and management6(02), 235-255.
  21. Yao, R., Mei, F., & Wei, X. (2021). Healthcare supply chain for China’s Belt and Road Initiative during COVID-19. 2021 5thinternational conference on advances in energy, environment and chemical science (AEECS 2021) (Vol. 245, p. 03044). EDP Sciences.
  22. Chang, D. S., Liu, S. M., & Chen, Y. C. (2017). Applying DEMATEL to assess TRIZ’s inventive principles for resolving contradictions in the long-term care cloud system. Industrial management & data systems, 117(6), 1244-1262.
  23. Zadeth, L. A. (1965). Fuzzy sets. Information and control8(3), 338-353.
  24. Atanassov, K. (2016). Intuitionistic fuzzy sets. International journal bioautomation, 20(S1), S1-S6.
  25. Yager, R. R. (2013, June). Pythagorean fuzzy subsets. 2013 joint IFSA world congress and NAFIPS annual meeting (IFSA/NAFIPS)(pp. 57-61). IEEE. DOI: 1109/IFSA-NAFIPS.2013.6608375
  26. Farhadinia, B. (2021). Similarity-based multi-criteria decision making technique of pythagorean fuzzy sets. Artificial intelligence review, 1-46.
  27. Ejegwa, P. A., & Onyeke, I. C. (2020). Medical diagnostic analysis on some selected patients based on modified Thao et al.’s correlation coefficient of intuitionistic fuzzy sets via an algorithmic approach. Journal of fuzzy extension and applications1(2), 122-132. DOI: 22105/jfea.2020.250108.1014
  28. Ejegwa, P. A., Adah, V., & Onyeke, I. C. (2021). Some modified Pythagorean fuzzy correlation measures with application in determining some selected decision-making problems. Granular computing, 1-11.
  29. Ejegwa, P. A., & Awolola, J. A. (2021). Real-life decision making based on a new correlation coefficient in Pythagorean fuzzy environment. Ann fuzzy math inform21(1), 51-67.
  30. Ejegwa, P. A., & Awolola, J. A. (2021). Novel distance measures for Pythagorean fuzzy sets with applications to pattern recognition problems. Granular computing6(1), 181-189.
  31. Ejegwa, P. A. (2021). Generalized triparametric correlation coefficient for Pythagorean fuzzy sets with application to MCDM problems. Granular computing6(3), 557-566.