Optimum Disaster Warehouse Location Selection for Sustainable Disaster Logistics: Case of Giresun Province

Yıl 2020, Cilt: 6 Sayı: 1, 144 - 165, 22.05.2020

Mustafa Ergün Selçuk Korucuk Salih Memiş

https://doi.org/10.28979/comufbed.686301

Cited By: 13

Öz

The disasters that occur are mostly unpredictable, but it is necessary to take precautions, assuming that they may occur at any time. When the disaster occurs, interventions and supply of necessary materials are very important applications which directly affect the survival of the victims. Accurate, dynamic and fast delivery of the necessary material depends on careful examination and analysis of the criteria for optimum disaster warehouse site selection. Incorrect determination of the storage locations means that the aid cannot be sent to the disaster area or the aid sent to the incorrect location. This situation may result in wasted efforts and disaster victims not getting any help. In this study, sustainable disaster logistics criteria were evaluated in the selection of disaster warehouse locations in Giresun and optimum disaster warehouse locations were selected. For this purpose, weighting of the criteria which are the determinants of alternative disaster warehouse site alternatives has been determined by AHP (Analytic Hierarchy Process) method. Then, AHP based MAUT (Multi Attribute Utility Theory) and SAW (Simple Additive Weighting) methods were used to select the optimum disaster warehouse location. As a result of the evaluation, “infrastructure” was the most important criteria for the selection of the optimum disaster warehouse location. The most important sub-criterion was “Disaster Structure”. On the other hand, it was determined that the sustainable optimum disaster warehouse location selection was “A2”, which was obtained by both MAUT and SAW methods.

Anahtar Kelimeler

Warehouse location selection, Diaster logistics, AHP, MAUT, SAW

Sürdürülebilir Afet Lojistiğine Yönelik İdeal Afet Depo Yeri Seçimi: Giresun İli Örneği

Öz

Meydana gelen felaketler çoğunlukla öngörülemez ama her an oluşabileceği varsayılarak önlem almak gerekmektedir. Afet durumunda ihtiyaç duyulan malzemelerin temin edilmesi ve gerekli müdahalelerin yapılması, afete maruz kalan kimselerin hayatta kalmasına doğrudan etki eden çok önemli uygulamalardır. Özellikle gerekli olan malzemenin doğru ve süratli bir biçimde yerine ulaştırılması, ideal afet depo yeri seçimi bileşenlerinin dikkatli bir biçimde incelenmesi ve analiz edilmesine bağlıdır. Depo yerlerinin yanlış belirlenmesi demek, afet bölgesine yardım gönderilememesi veya gönderilen yardımların doğru yere ulaş-maması anlamına gelmektedir. Bu durum yapılan bütün uğraşların boşa gitmesi ve afetzedelerin gerekli yardımları alamamalarıyla sonuçlanabilir. Bu çalışmada, sürdürülebilir afet lojistiğine yönelik Giresun’da ideal afet depo yeri seçimi ölçütleri değerlendirilmiş ve ideal afet depo yeri seçimi yapılmıştır. Bu amaçla sürdürülebilir ideal afet depo yeri seçimi alternatiflerinin belirleyicisi olan ölçütlerin ağırlıklandırılması, AHS (Analitik Hiyerarşi Süreci) yöntemi ile yapılmıştır. Daha sonra, AHS temelli MAUT (Multi Attribute Utility Theory-Çok Nitelikli Karar Verme) ve SAW (Simple Additive Weighting-Basit Toplamlı Ağırlıklan-dırma) yöntemleriyle ideal afet depo yeri seçimi gerçekleştirilmiştir. Yapılan değerlendirme sonucunda ideal afet depo yeri seçimi ölçütlerinin en önemlisi “Altyapı” olmuştur. En önemli alt ölçütün ise “Afetsel-lik Yapısı” olduğu görülmüştür. Öte yandan hem MAUT hem de SAW yöntemleri ile elde edilen sonuçlara göre sürdürülebilir ideal afet depo yerinin “A2” olduğu saptanmıştır.

Anahtar Kelimeler

Depo yeri seçimi, Afet lojistiği, AHS, MAUT, SAW

Kaynakça

• Abbasi, M. ve Nilsson, F. (2016). Developing environmentally sustainable logistics: Exploring themes and challenges from a logistics service providers’ perspective. Transportation Research Part D: Transport and Environment, 46, 273-283.
• Ashinaka, T., Kubo, M. ve Namatame, A. (2016). A decision-support tool for humanitarian logistics. Gen, M., Katai, O., McKay, B., Namatame, A., Sarker, R.A., Zhang, B.-T. (Editörler) In Intelli-gent and Evolutionary Systems (s.293-304). Springer, Cham, 293-304.
• Awasti, A., Chauhan, S.S. ve Goyal, S.K. (2011). A multi-criteria decision making approach for loca-tion planning for urban distribution centers under uncertainty. Mathematical and Computer Modelling, 53, 98-109.
• Aydın, H., Ayvaz, B. ve Küçükaşçı, E. Ş. (2017). Afet yönetiminde lojistik depo seçimi problemi: Maltepe ilçesi örneği. Journal of Yaşar University, 12, 1-13.
• Balcik, B. ve Beamon, B. M. (2008). Facility location in humanitarian relief. International Journal of Logistics, 11(2), 101-121.
• Barbarosoğlu, G., Özdamar, L. ve Cevik, A. (2002). An interactive approach for hierarchical analysis of helicopter logistics in disaster relief operations. European Journal of Operational Rese-arch, 140(1), 118-133.
• Boltürk, E., Onar Çevik, S., Öztayşi, B. ve Kahraman, C. (2016). Multi-attribute warehouse location selection in humanitarian logistics using hesitant fuzzy AHP, International Journal of the Analytic Hierarchy Process, 8(2), 271-298.
• Boonmee, C., Arimura, M. ve Asada, T. (2017). Facility location optimization model for emergency humanitarian logistics. International Journal of Disaster Risk Reduction, 24, 485-498.
• Chan F.T.S., Kumar, N. ve Choy, K. L. (2007). Decision making approach for the distribution centre location problem in an supply chain network using the fuzzy-based hierarchical concept. Jour-nal of Engineering Manufacture, 221 (B), 725-739.
• Chanta, S. ve Sangsawang, O. (2012). Shelter-site selection during flood disaster. Lecture Notes in Management Science, 4, 282-288.
• Christopher, M. (2011). Logistics, the supply chain and competitive strategy. In Logistics and Supply Chain Management (4. Baskı.). Prentice Hall. London, Pearson Education.
• Davis, L. B., Samanlıoglu, F., Qu, X. ve Root, S. (2013). Inventory planning and coordination in di-saster relief efforts. International Journal of Production Economics, 141(2), 561-573.
• Demirdöğen, O., Erdal, H., Yazıcılar, F. ve Aykol, S. (2017). Disaster logistics facility location prob-lem: An application for TR A1 region. The International New Issues in Social Sciences, 5(5), 323-342.
• Dubey, R. ve Gunasekaran, A. (2016). The sustainable humanitarian supply chain design: Agility, adaptability and alignment. International Journal of Logistics Research and Applicati-ons, 19(1), 62-82.
• Dwyer, A., Zoppou, C., Nielsen, O., Day, S. ve Roberts, S. (2004). Quantifying social vulnerability: A methodology for identifying those at risk to natural hazards. Canberra: Geoscience Australia.
• Eleren, A. (2006). Kuruluş yeri seçiminin analitik hiyerarşi süreci yöntemi ile belirlenmesi; Deri sektö-rü örneği. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 20 (2), 405-416.
• Flanagan, B. E., Gregory, E. W., Hallisey, E. J., Heitgerd, J. L. ve Lewis, B. (2011). A social vulnerabi-lity index for disaster management. Journal of Homeland Security and Emergency Manage-ment, 8(1), 1-22.
• Florez, J. V., Lauras, M., Okongwu, U. ve Dupont, L. (2015). A decision support system for robust humanitarian facility location. Engineering Applications of Artificial Intelligence, 46, 326-335.
• Hadiguna, R. A., Kamil, I., Delati, A. ve Reed, R. (2014). Implementing a web-based decision support system for disaster logistics: A case study of an evacuation location assessment for Indonesia. International Journal of Disaster Risk Reduction, 9, 38-47.
• Hale, T. ve Moberg, C. R. (2005). Improving supply chain disaster preparedness: A decision process for secure site location. International Journal of Physical Distribution ve Logistics Manage-ment, 35(3), 195-207.
• Handayani, N. U., Rinawati, D. I. ve Wiguna, Y. K. (2015). Model of pre-positioning warehouse logis-tics for disaster eruption of Mount Merapi in Sleman Yogyakarta. In Proceedings of the Joint International Conference on Electric Vehicular Technology and Industrial, Mechanical, Electri-cal and Chemical Engineering (ICEVT & IMECE) (pp. 401-405). IEEE.
• Iqbal, S., Sardar, M. U., Lodhi, F. K. ve Hasan, O. (2018). Statistical model checking of relief supply location and distribution in natural disaster management. International Journal of Disaster Risk Reduction, 31, 1043-1053.
• Ishizaka, A. ve Nemery, P. (2013). Multi-criteria decision analysis: methods and software, John Wiley & Sons Ltd. Published, Chichester/UK.
• Janic, M. ve Reggiani, A. (2002). An application of the multiple criteria decision making (mcdm) analysis to the selection of a new hub airport. European Journal of Transport and Infrastructu-re Research, 2(2), 113-141.
• Kim, S. K. ve Song, O. (2009). A maut approach for selecting a dismantling scenario for the thermal column in krr-1. Annals of Nuclear Energy, 36(2), 145-150.
• Kobu, B. (2008). Üretim yönetimi, genişletilmiş güncellenmiş 14.baskı, Beta Basım Yayım, İstanbul.
• Konuşkan, Ö. ve Uygun, Ö. (2014). Çok nitelikli karar verme (maut) yöntemi ve bir uygulaması, ISI-TES 2014, 1403-1412.
• Kovács, G. ve Spens, K. M. (2007). Humanitarian logistics in disaster relief operations. International Journal of Physical Distribution ve Logistics Management, 37(2), 99-114.
• Kuo, M. S. ve Liang, G. S. (2011). Combining vikor with gra techniques to evaluate service quality of airports under fuzzy environment. Expert Systems with Applications, 38(3), 1304-1312.
• Kusumastuti, R. D., Wibowo, S. S. ve Insanita, R. (2013). Modeling facility locations for relief logis-tics in Indonesia. In Humanitarian and Relief Logistics (pp. 183-205). Springer, New York, NY.
• Lee, D. H., Dong, M. ve Bian, W. (2010). The design of sustainable logistics network under uncerta-inty. International Journal of Production Economics, 128(1), 159-166.
• Liu, C., Chen, Z. H. ve Gong, Y. Y. (2013). Site selection of emergency material warehouse under fuzzy environment. Journal of Central South University, 20(6), 1610-1615.
• Loken E. ve Botterud A. (2007). Planning of mixed local energy distribution systems: A comparison of two multi-criteria decision methods, 28thAnnual IAEE International Conference, Taipei, Taiwan, 1586-1587.
• Loree, N. ve Aros-Vera, F. (2018). Points of distribution location and inventory management model for post-disaster humanitarian logistics. Transportation Research Part E: Logistics and Trans-portation Review, 116, 1-24.
• Maharjan, R. ve Hanaoka, S. (2017). Warehouse location determination for humanitarian relief distri-bution in Nepal. Transportation Research Procedia, 25, 1151-1163.
• Mete, H. O. ve Zabinsky, Z. B. (2010). Stochastic optimization of medical supply location and distri-bution in disaster management. International Journal of Production Economics, 126(1), 76-84.
• Neto, J. Q. F., Bloemhof-Ruwaard, J. M., Van Nunen, J. A. ve Van Heck, E. (2008). Designing and evaluating sustainable logistics networks. International Journal of Production Econo-mics, 111(2), 195-208.
• O'brien, G., O'keefe, P., Rose, J. ve Wisner, B. (2006). Climate change and disaster management. Disasters, 30(1), 64-80.
• Ofluoglu, A., Baki, B. ve Ar, İ. M. (2017). Multi-criteria decision analysis model for warehouse loca-tion in disaster logistics. Journal of Management Marketing and Logistics, 4(2), 89-106.
• Özdamar, L. ve Demir, O. (2012). A hierarchical clustering and routing procedure for large scale disaster relief logistics planning. Transportation Research Part E: Logistics and Transportation Review, 48(3), 591-602.
• Özdamar, L., Ekinci, E. ve Küçükyazıcı, B. (2004). Emergency logistics planning in natural disas-ters. Annals of Operations Research, 129(1-4), 217-245.
• Peker, İ., Korucuk, S., Ulutaş, Ş., Okatan Sayın, B. ve Yaşar, F., (2016). Afet lojistiği kapsamında en uygun dağıtım merkez yerinin ahs-vikor bütünleşik yöntemi ile belirlenmesi: Erzincan ili örneği. Yönetim ve Ekonomi Araştırmaları Dergisi,14(1), 82-103.
• Ramos, T. R. P., Gomes, M. I. ve Barbosa-Póvoa, A. P. (2014). Planning a sustainable reverse logis-tics system: Balancing costs with environmental and social concerns. Omega, 48, 60-74.
• Rath, S. ve Gutjahr, W. J. (2014). A math-heuristic for the warehouse location–routing problem in disaster relief. Computers & Operations Research, 42, 25-39.
• Rodríguez-Espíndola, O., Albores, P. ve Brewster, C. (2018). Disaster preparedness in humanitarian logistics: A collaborative approach for resource management in floods. European Journal of Operational Research, 264(3), 978-993.
• Roh, S. Y., Jang, H. M. ve Han, C. H. (2013). Warehouse location decision factors in humanitarian relief logistics. The Asian Journal of Shipping and Logistics, 29(1), 103-120.
• Roh, S., Pettit, S., Harris, I. ve 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.
• Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Ser-vices Sciences, 1 (1), 83-98.
• Saaty, T. L., ve Niemira, M. P. (2006). A framework for making a better decision. Research Re-view, 13(1), 1-4.
• Salam, M. A. (2006). Disaster logistics management, https://www.poms mee-tings.org/confpapers/005/005-0009.doc, (12.01.2019).
• Savitha, K. ve Chandrasekar, C. (2011). Trusted network selection using saw and topsıs algorithms for heterogeneous wireless networks, International Journal of Computer Applications, 26(8), 22-29.
• Schulz, S. F. ve Blecken, A. (2010). Horizontal cooperation in disaster relief logistics: benefits and ımpediments. International Journal of Physical Distribution & Logistics Management, 40(8/9), 636-656.
• Seraji, H., Tavakkoli-Moghaddam, R. ve Soltani, R. (2019). A two-stage mathematical model for eva-cuation planning and relief logistics in a response phase. Journal of Industrial and Systems Engineering, 12(1), 129-146.
• Sheu, J. B. (2007). An emergency logistics distribution approach for quick response to urgent relief demand in disasters. Transportation Research Part E: Logistics and Transportation Re-view, 43(6), 687-709.
• Şahin, S. (2017). Çok kriterli karar verme yöntemleri ile bulanık ortamda afet yönetimi sisteminde ge-çici barınma alanları yer seçimi (Yayınlanmamış Yüksek Lisans Tezi, İstanbul Ticaret Üniver-sitesi).
• Temiz, H. (2018). Afet yönetiminde lojistik depo seçimi ve bir uygulaması (Yayınlanmamış Yüksek Lisans Tezi, İstanbul Ticaret Üniversitesi).
• Tunca, Z. Ömürbek N. Cömert G. ve Aksoy E. (2016). Opec ülkelerinin performanslarının çok kriterli karar verme yöntemlerinden entropi ve maut ile değerlendirilmesi. Süleyman Demirel Üniver-sitesi Vizyoner Dergisi, 7(14), 1-12.
• Van Wassenhove, L. N. (2006). Humanitarian aid logistics: supply chain management in high gear. Journal of the Operational Research Society, 57(5), 475-489.
• Wang, B. C., Li, M., Hu, Y., Huang, L. ve Lin, S. M. (2018). Optimizing locations and scales of emer-gency warehouses based on damage scenarios. Journal of the Operations Research Society of China, 1-20.
• Xifeng, T., Ji, Z. ve Peng, X. (2013). A multi-objective optimization model for sustainable logistics facility location. Transportation Research Part D: Transport and Environment, 22, 45-48.
• Yang, H., Yang, L. ve Yang, S. (2011). Hybrid zigbee rfid sensor network for humanitarian logistics centre management, Journal of Network and Computer Applications, (34), 938-948.
• Yeh, C. H. (2003). The selection of multi-attribute decision making methods for scholarship student selection,International Journal of Selection and Assessment, 11(4), 289-296.
• Zadeh, L.A. (1965). Fuzzy sets. Information and Control, 8, 338-353.