İnsani Yardım Depolarının Değerlendirilmesi için Bulanık Mantığın Entegre Edildiği Çok Kriterli Karar Verme Yaklaşımı: Suriye’de Bir Uygulama Çalışması

2019 yılının sonunda, Koronavirüs Hastalığı (COVID-19) olarak adlandırılan yeni bir afet insanlığa karşı ortaya çıkmış ve tüm dünyaya yayılmıştır. En gelişmiş ülkeler, bu pandemiden daha fazla etkilenmiştir. Fakat, Suriye’de olduğu gibi bir çatışmanın yaşandığı/yaşanmakta olduğu ülkeler için durum daha karmaşıktır. Suriye’de, çatışma 9 yıldan fazla bir süredir devam etmektedir ve ülke dâhilinde 6 milyondan fazla ülke içinde yerlerinden edilmiş insan vardır. Bu durum, milyonlarca insanın zor koşullarda yaşadığını ve sağlık hizmeti, barınma, yiyecek, güvenlik ve ilgili diğer yaşamsal ihtiyaçların arayışında olduklarını göstermektedir. Bu bağlamda, bir pandemi boyunca malzemelerin ve yardım setlerinin korunmak ve daha sonra pandemiden en çok etkilenmiş insanlara etkili bir şekilde dağıtımını yapmak için bir insani yardım deposunda saklanmaları gerektiğinden ötürü, bu çalışmada, yardım depolarının yerleşiminin araştırmasına odaklanılmıştır. En uygun yeri belirlemek için yardım depolarının lokasyonları bilimsel insani yardıma dayalı hibrid bir metodoloji ile değerlendirilmiştir. Bu yeni metodoloji Suriye/Halep’in kuzeyinde gerçek bir vaka çalışmasına uygulanmıştır. Bu amaçla, öncelikle, veri, doğrudan hedef bölgeden toplanmıştır; akabinde çalışmaya dahil edilecek insani ve ekonomik kriterler üç uzman tarafından seçilmiştir. Kriter ağırlıkları Bulanık Analitik Hiyerarşi Prosesi (B-AHP) ile hesaplanmıştır. Son olarak, aday depoları değerlendirmek ve sıralamak için bir Çok Kriterli Karar Verme (ÇKKV) yöntemi olan MULTIMOORA yöntemi uygulanmıştır. Önerilen metodoloji yardım depolarını değerlendirmede etkinliğini ve etkililiğini göstermiştir ve karar verme sürecini hızlandırmak için kullanılabilir. Bunun neticesinde afetten etkilenen insanların acıları azaltılabilir ve hedef bölgedeki bağışların yüksek etkinliği başarılabilir.

Anahtar Kelimeler:

COVID-19, Pandemic, Warehouse Location, Multi Crtieria Decision Making (MCDM), Fuzzy Analytic Hierarchy Process (F-AHP), MULTIMOORA

Multi Criteria Decision Making Approach to the Evaluation of Humanitarian Relief Warehouses Integrating Fuzzy Logic: A Case Study in Syria

A new disaster to humanity, called Coronavirus Disease (COVID-19), arose in and spread to worldwide at late December 2019. The most developed countries are affected from this pandemic more. However, the situation is more complex in some countries that are witnessed/witnessing a conflict, as in Syria. In Syria, the conflict continues more than 9 years and within the country there are more than 6 million internally displaced people (IDPs). This situation signifies millions of people living in hard conditions and seeking healthcare service, sheltering, food, safety and other related vital needs. In this context, since during a pandemic supplies and aid kits need to be stockpiled in a humanitarian relief warehouse to be protected and then distributed effectively to the most pandemic-affected people, we focused on the location research of relief warehouses in this study. We evaluated the locations of the relief warehouses to determine the most appropriate location based on a scientific humanitarian aid-based hybrid methodology. This novel methodology is implemented to a real case study in north of Aleppo/Syria. For this aim, firstly, data is collected directly from the target area; then humanitarian and economic criteria are selected by three experts to be included in the study. Criteria weights are computed by the Fuzzy Analytic Hierarchy Process (F-AHP). Finally, MULTIMOORA technique as a Multi Criteria Decision Making (MCDM) method is applied to assess the candidate warehouses and rank them. The proposed methodology showed its efficiency and effectiveness in evaluating relief warehouses and it can be utilized to facilitate the decision-making process. As a result, the suffering of the disaster-affected people can be reduced and high efficiency from donations in the target area can be achieved.

Keywords:

COVID-19, Pandemic, Warehouse Location, Multi Criteria Decision Making (MCDM), Fuzzy Analytic Hierarchy Process (F-AHP), MULTIMOORA,

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Aghezzaf, E. (2005). Capacity planning and warehouse location in supply chains with uncertain demands. Journal of the Operational Research Society, 56(4), 453-462. https://doi.org/10.1057/palgrave.jors.2601834
Ballou, R. H. (1968). Dynamic warehouse location analysis. Journal of Marketing Research, 5(3), 271-276. https://doi.org/10.1177/002224376800500304
Brauers, W. K. M., & Zavadskas, E. K. (2006). The MOORA method and its application to privatization in a transition economy. Control and Cybernetics, 35, 445–469.
Brauers, W. K. M., & Zavadskas, E. K. (2010). Project management by MULTIMOORA as an instrument for transition economies. Technological and Economic Development of Economy, 16(1), 5–24. https://doi.org/10.3846/tede.2010.01
Brauers, W. K. M., & Zavadskas, E. K. (2012). Robustness of MULTIMOORA: a method for multi-objective optimization. Informatica, 23(1), 1–25. https://doi.org/10.15388/Informatica.2012.346
Brauers, W. K. M., & Zavadskas, E. K. (2013). Multi-objective decision making with a large number of objectives:. An application for Europe. International Journal of Operations Research , 10(2), 67-79. https://doi.org/10.15388/Informatica.2012.346
Buckley, J. J. (1985). Ranking alternatives using fuzzy numbers. Fuzzy Sets and Systems, 15(1), 21-31. https://doi.org/10.1016/0165-0114(85)90013-2
Chatterjee, K., & Kar, S. (2013, December). An Induced Fuzzy Rasch-VIKOR model for Warehouse Location evaluation under Risky Supply chain. In International Conference on Pattern Recognition and Machine Intelligence (pp. 714-719). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45062-4_101
Chen, C. L., Yuan, T. W., & Lee, W. C. (2007). Multi-criteria fuzzy optimization for locating warehouses and distribution centers in a supply chain network. Journal of the Chinese Institute of Chemical Engineers, 38(5-6), 393-407. https://doi.org/10.1016/j.jcice.2007.08.001
ÇALIK, A. (2020). Depo Yeri Seçimi için Aralık Tip-2 Bulanık ÇKKV Tabanlı Hibrit Bir Yaklaşım. MANAS Sosyal Araştırmalar Dergisi, 9(1), 101-114.
Danchuk, V., Bakulich, O., & Svatko, V. (2018). Identifying warehouse location using the radiation therapy method in logistic distribution system. Transport Problems, 13(4), 144-155. https://doi.org/10.20858/tp.2018.13.4.13
Dey, B., Bairagi, B., Sarkar, B., & Sanyal, S. K. (2015). Warehouse location selection by fuzzy multi-criteria decision making methodologies based on subjective and objective criteria. International Journal of Management Science and Engineering Management, 11(4), 262-278. https://doi.org/10.1080/17509653.2015.1086964
Di Gennaro, F., Pizzol, D., Marotta, C., Antunes, M., Racalbuto, V., Veronese, N., & Smith, L. (2020). Coronavirus diseases (COVID-19) current status and future perspectives: a narrative review. International journal of environmental research and public health, 17(8), 2690.
Emeç, Ş., & Akkaya, G. (2019). A stochastic multi-criteria decision-making analysis for a warehouse location selection problem: a case study. International Journal of Research-Granthaalayah, 7(12), 133-143. https://doi.org/10.29121/granthaalayah.v7.i12.2019.307
Hakim, R. T., & Kusumastuti, R. D. (2018). A model to determine relief warehouse location in east jakarta using the analytic hierarchy process. International Journal of Technology, 9(7), 1405-1414. https://doi.org/10.14716/ijtech.v9i7.1596
Huang, S., Wang, Q., Batta, R., & Nagi, R. (2015). An integrated model for site selection and space determination of warehouses. Computers & Operations Research, 62, 169-176. https://doi.org/10.1016/j.cor.2014.10.015
Huifeng, J., & Aigong, X. (2008). The method of warehouse location selection based on GIS and remote sensing images. Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVII. Part B, 2(3), 545-548.
Khaengkhan, M., Hotrawisaya, C., Kiranantawat, B., & Shaharudin, M. R. (2019). Comparative analysis of multiple criteria decision making (MCDM) approach in warehouse location selection of agricultural products in Thailand. International Journal of Supply Chain Management, 8(5), 168-175.
Kudláčková, N., & Chocholáč, J. (2017). Warehouse location problem in context of delivery time shortening. In MATEC Web of Conferences, Vol. 134, 18th International Scientific Conference-LOGI 2017. EDP Sciences.
Lee, C. Y. (1993). The multiproduct warehouse location problem: Applying a decomposition algorithm. International Journal of Physical Distribution & Logistics Management, 23(6), 3-13. https://doi.org/10.1108/09600039310044858
Malmir, B., Aghighi, A., Bisheh, M. N., Ala, A., Avilaq, B. A., & Dehghani, S. (2015). Application of a new multi criteria decision making method for warehouse location problem. International Journal of Value Chain Management, 7(3), 255-270. https://doi.org/10.1504/IJVCM.2016.079211
Miç, P., Koyuncu, M., & Hallak, J. (2019). Primary health care center (PHCC) location-allocation with multi-objective modelling: a case study in Idleb, Syria. International journal of environmental research and public health, 16(5), 811.
Monthatipkul, C. (2016). A non-linear program to find an approximate location of a second warehouse: A case study. Kasetsart Journal of Social Sciences, 37(3), 190-201. https://doi.org/10.1016/j.kjss.2016.08.007
OCHA (2020). United Nations Office for the Coordination of Humanitarian Affairs, Syrian Arap Republic COVID-19 Response Update No.13. https://reliefweb.int/sites/reliefweb.int/files/resources/covid_response_update_no._13%20%281%29.pdf (Accessed date: 19.12.2020)
Ofluoglu, A., Baki, B., & Ar, I. (2017). Multi-criteria decision analysis model for warehouse location in disaster logistics. Journal of Management Marketing and Logistics, 4(2), 89-106. https://doi.org/10.17261/Pressacademia.2017.454
Roh, S., Pettit, S., Harris, I., & Beresford, A. (2015). The pre-positioning of warehouses at regional and local levels for a humanitarian relief organisation. International Journal of Production Economics, 170, 616-628. https://doi.org/10.1016/j.ijpe.2015.01.015
Roh, S. Y., Shin, Y. R., & Seo, Y. J. (2018). The Pre-positioned warehouse location selection for international humanitarian relief logistics. The Asian Journal of Shipping and Logistics, 34(4), 297-307.
Trivedi, A., & Singh, A. (2014). Multi criteria selection of potential warehouse locations in humanitarian relief logistics, 14. Global Conference on Flexible Systems Management, Singapore.
Tsaur, S. H., Chiu, Y. C., & Huang, C. H. (2002). Determinants of guest loyalty to international tourist hotels- a neural network approach. Tourism Management, 23(4), 397-405. https://doi.org/10.1016/S0261-5177(01)00097-8
Wang, W., Gao, J., Gao, T., & Zhao, H. (2017, June). Optimization of Automated Warehouse Location Based on Genetic Algorithm. In 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017). Atlantis Press. https://doi.org/10.2991/caai-17.2017.70
www.worldometers.info/coronavirus/ Access Date: 30/12/2020
www.statista.com Access Date: 27/12/2020
www.ourworldindata.org Access Date: 27/12/2020
Yen, M. Y. , Schwartz, J., King, C. C., Lee, C. M., Hsueh, P. R., & Society of Taiwan Long-term Care Infection Prevention and Control (2020). Recommendations for protecting against and mitigating the COVID-19 pandemic in long-term care facilities. Journal of Microbiology, Immunology and Infection, 53(3), 447-453. https://doi.org/10.1016/j.jmii.2020.04.003
Yuan, Q. (2019). Does context matter in environmental justice patterns? Evidence on warehousing location from four metro areas in California. Land Use Policy, 82, 328-338. https://doi.org/10.1016/j.landusepol.2018.12.011
Zhang, Y., & Swaminathan, J. M. (2020). Warehouse Location in An Emerging Country: A Win–Win Proposition? Production and Operations Management 29(6), 1487-1505. https://doi.org/10.1111/poms.13169