MULTI-CRITERIA DECISION ANALYSIS MODEL FOR WAREHOUSE LOCATION IN DISASTER LOGISTICS

Year 2017, , 89 - 106, 30.06.2017

Aylin Ofluoglu Birdogan Baki , İlker Murat Ar

https://doi.org/10.17261/Pressacademia.2017.454

Abstract

Purpose-
Optimum location of warehouses for disaster logistics
increases the performance of humanitarian logistics, so that all the
needs of beneficiaries can be delivered in a short time. In this paper, a
multi-criteria decision analysis model is designed to decide the best warehouse
location in disaster logistics in Trabzon, Turkey.

Methodology-
Entropy is used to
determine the important weights of the criteria. The SAW, TOPSIS and VIKOR
methods are utilized to rank the warehouse location alternatives. The ranking
results are also combined by the Borda
Count method to obtain a final ranking. The robustness of the model is
examined by the sensitivity analysis.

Findings- According
to the results of the analysis, the disaster situation of the land, land size,
and the distance of settlement were determined as the three most important
criteria. Sensitivity analysis also proved 
the robustness of the model.

Conclusion- The
results are consistent with the expectations of the decision makers and seem to
support their decisions.
For future studies, the results can be compared using fuzzy
techniques.

 

Keywords

Disaster logistics, warehouse location decision, multicriteria decision making techniques

References

• Ahmadi, M., Seifi, A. ve Tootooni, B.2015, “A humanitarian logistics model for disaster relief operation considering network failure and standard relief time: A case study on San Francisco district”, Transportation Research Part E, 75, pp.145-163.
• Ağdaş, M., Bali, Ö. ve Ballı, H.2014, “Afet Lojistiği Kapsamında Dağıtım Merkezi İçin Yer Seçimi: SMAA-2 Tekniği ile Bir Uygulama”, Beykoz Akademi Dergisi, 2(1), pp. 75-95.
• Akgün, İ., Gümüşbuğa, F. ve Tansel, B.2015, “Risk based facility location by using fault tree analysis in disaster management”, Omega, 52, pp. 168179.
• Alberto, P. 2000, “The logistics of industrial location decisions: An application of the analytical hierarchy process methodology”, International Journal of Logistics: Research and Application, 3(3), pp. 273-289.
• Alinezhad, A. ve Amini, A.2011, “Sensitivity Analysis of TOPSIS Technique: The Results of Change in the Weight of One Attribute on the Final Ranking of Alternatives”, Journal of Optimization in Industrial Engineering, 7, pp. 23-28.
• Aslan, H.M., ve Yıldız, M.S.2016, “Yapay zekâ Optimizasyon Yöntemi İle Yaralı Toplama Merkezlerinin Konuşlandırılması”, Bartın Üniversitesi İİBF Dergisi, 7(14), pp. 165-188.
• Awasthi, A., Chauhan, S.S. ve Goyal, S.K. 2011, “A multi-criteria decision making approach for location planning for urban distribution centers under uncertainty”, Mathematical and Computer Modelling, 53, pp. 98-109.
• Balcık, B. ve Beamon, B.M.2008, “Facility location in humanitarian relief”, International Journal of Logistics: Research and Applications, 11(2), pp. 101-121.
• Campbell, A.M. ve Jones, P.C.2011, “Prepositioning supplies in preparation for disasters”, European Journal of Operational Research, 209, pp. 156165.
• Çakır, S. ve Perçin, S. 2013, “AB Ülkeleri’nde Bütünleşik Entropi Ağırlık-TOPSIS Yöntemiyle AR-GE Performansının Ölçülmesi”, Uludağ Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 32(1), pp. 77-95.
• Çakır, S. ve Perçin, S. 2013, “Çok Kriterli Karar Verme Teknikleriyle Lojistik Firmalarında Performans Ölçümü”, Ege Akademik Bakış, 13(4), pp. 449459.
• Dekle, J., Lavieri, M.S., Martin, E., Emir-Farinas, H. ve Francis, R.L.2005, “A Florida County Locates Disaster Recovery Centers”, Interfaces, 35( 2), pp. 133-139.
• Demirel, T., Demirel, N.Ç. ve Kahraman, C.2010 “Multi-criteria warehouse location selection using Choquet integral”, Expert Systems with Applications, 37, pp. 3943-3952.
• EMDAT. 2016, http://www.emdat.be/advanced_search/index.html, (24.06.2016).
• Ersoy, Ş.2016, “2015 Yılının Doğa Kaynaklı Afetleri ‘Dünya ve Türkiye’”, TMMOB Jeoloji Mühendisleri Odası Yayınları, Ankara.
• Fikar, C., Gronalt, M. ve Hirsch, P.2016, “A decision support system for coordinated disaster relief distribution”, Expert Systems with Applications, 57, pp. 104-116.
• Gatignon, A., Van Wassenhove, L.N. ve Charles, A.2010, “The Yogyakarta earthquake: Humanitarian relief through IFRC’s decentralized supply chain”, International Journal of Production Economics, 126, pp. 102-110.
• Görener, A.2011, “Bütünleşik ANP-VIKOR Yaklaşımıile ERP Yazılımı Seçimi”, Havacılık ve Uzay Teknolojileri Dergisi, 5(1), pp. 97-110.
• Gözaydın, O. ve Can, T.2013, “Deprem Yardım İstasyonları İçin Lojistik Merkezi Seçimi: Türkiye Örneği”, Havacılık ve Uzay Teknolojileri Dergisi, 6(2),pp. 17-31.
• 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, pp. 38-47.
• Hiltunen, V., Kangas, J. ve Pykäläinen, J.2008, “Voting methods in strategic forest planning - Experiences from Metsähallitus”, Forest Policy and Economics, 10, pp. 117-127.
• Hong, L. ve Xiaohua, Z.2011, “Study on location selection of multi-objective emergency logistics center based on AHP”, Procedia Engineering, 15, pp. 2128-2132.
• Kandel, C. Abidi, H. ve Klumpp, M.2011, “Humanitarian Logistics Depot Location Model”, The 2011 European Simulation and Modelling Conference, Conference Proceedings October 24-26, 2011 at University of Mino, Guimaraes, Portugal, pp. 288-293.
• Karaatlı, M.2016, “Entropi-Gri İlişkisel Analiz Yöntemleri ile Bütünleşik Bir Yaklaşım: Turizm Sektöründe Uygulama”, Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 21(1), pp. 63-77.
• Karami, A.2011, “Utilization And Comparison Of Multi Attribute Decision Making Techniques To Rank Bayesian Network Options”, Master Thesis, Skövde-Sweden, University of Skövde, School of Humanities and Informatics.
• Kayikci, Y.2010, “A conceptual model for intermodal freight logistics centre location decisions”, Procedia Social and Behavioral Sciences, 2, pp. 6297-6311.
• Korpela, J. ve Tuominen, M.1996, “A decision aid in warehouse site selection”, International Journal of Production Economics, 45, pp. 169-180.
• Kumar, P. ve Singh, R.K.2012, “A fuzzy AHP and TOPSIS methodology to evaluate 3PL in a supply chain”, Journal of Modelling in Management, 7(3), pp. 287-303.
• Lee, W.-S., Kim,B.S. ve Opit, P.F.2014, “A stock pre-positioning model to maximize the total expected relief demand of disaster areas”, Industrial Engineering and Management Systems, 13(3), pp. 297-303.
• Li, X., Wang, K., Liu, L. ve Xin, J.2011a, “Application of the Entropy Weight and TOPSIS Method in Safety Evaluation of Coal Mines”, Procedia Engineering, 26, pp. 2085-2091.
• Li, Y. Liu, X. ve Chen, Y.2011b, “Selection of logistics center location using Axiomatic Fuzzy Set and TOPSIS methodology in logistics management”, Expert Systems with Applications, 38, pp. 7901-7908.
• Lin, Y.H., Batta, R., Rogerson, P.A., Blatt, A. ve Flanigan, M.2012, “Location of temporary depots to facilitate relief operations after an earthquake”, Socio-Economic Planning Sciences, 46, pp. 112-123.
• Malekian, A. ve Azarnivand, A.2016, “Application of Integrated Shannon’s Entropy and VIKOR Techniques in Prioritization of Flood Risk in the Shemshak Watershed, Iran”, Water Resources Management, 30, pp.409-425.
• Mete, H.O. ve Zabinsky, Z.B. 2010, “Stochastic optimization of medical supply location and distribution in disaster management”, International Journal of Production Economics, 126, pp. 76-84.
• Na, H.S. ve Banerjee, A.,2015, “A disaster evacuation network model for transporting multiple priority evacuees”, IIE Transactions, 47(11), pp. 1287-1299.
• Nahleh, Y.A., Kumar, A. ve Daver, F.2013, “Facility Location Problem in Emergency Logistic”, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, 7(10), pp. 2113-2118.
• Opit, P.F. ve Nakade, K.2016, “Emergency response model of stock-prepositioning with transportation constraints”, IEEE International Conference on Industrial Engineering and Engineering Management, pp.239-243.
• Opricovic, S. ve Tzeng, G.H.2004, “Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS”, European Journal of Operational Research, 156, pp. 445-455.
• Opricovic, S. ve Tzeng, G.H.2007, “Extended VIKOR Method in Comparison with Other Outranking Methods”, European Journal of Operational Research, 178, pp. 514-529.
• Özcan, T., Çelebi, N. ve Esnaf, Ş.2011, “Comparative analysis of multi-criteria decision making methodologie and implementation of a warehouse location selection problem”, Expert Systems with Applications, 38, pp. 9773-9779.
• Peker, İ., Korucuk, S., Ulutaş, Ş., Sayın Okatan, B. ve Yaşar, F.2016, “Afet Lojistiği Kapsamında En Uygun Dağıtım Merkez Yerinin AHS-VIKOR Bütünleşik Yöntemi İle Belirlenmesi: Erzincan İli Örneği”, Yönetim ve Ekonomi Araştırmaları Dergisi, 14(1), pp. 82-103.
• Rat, S. ve Gutjahr, W.J.2014, “A math-heuristic for the warehouse location–routing problem in disaster relief”, Computers & Operations Research, 42, pp. 25-39.
• Rawls, C.G. ve Turnquist, M.A.2010, “Pre-positioning of emergency supplies for disaster response”, Transportation Research Part B, 44, pp. 521534.
• 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), pp. 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, pp. 616-628.
• Saeidian, B., Mesgari, M.S. ve Ghodousi, M.2016, “Evaluation and comparison of Genetic Algorithm and Bees Algorithm for location–allocation of earthquake relief centers”, International Journal of Disaster Risk Reduction, 15,pp. 94-107.
• Safeer, M., Anbuudayasankar, S.P., Balkumar, K. ve Ganesh, K.2014, “Analyzing transportation and distribution in emergency humanitarian logistics”, Procedia Engineering, 97, pp. 2248-2258.
• Salman, F.S. ve Yücel, E.2015, “Emergency facility location under random network damage: Insights from the Istanbul case”, Computers &Operations Research, 62, pp. 266-281
• Sarkis, J., Sundarraj, R.P.2002, “Hub Location At Digital Equipment Corporation: A Comprehensive Analysis of Qualitative And Quantitative Factors”, European Journal of Operational Research, 137, pp. 336-347.
• Shemshadi, A., Shirazi, H., Toreihi, M. ve Tarokh, M.J. 2011, “A fuzzy VIKOR method for supplier selection based on entropy measure for objective weighting”, Expert Systems with Applications, 38, pp. 12160-12167.
• Thomas, A. ve Kopczak, L.2005, “From Logistics to Supply Chain Management: the Path forward in the Humanitarian Sector”, Fritz Institute, San Francisco.
• Tofighi, S., Torabi, S.A. ve Mansouri, S.A.2016, “ Humanitarian logistics network design under mixed uncertainty”, European Journal of Operational Research, 250, pp. 239-250.
• Turgut, B.T., Taş, G., Herekoğlu, A., Tozan, H. ve Vayvay, O.2011, “A fuzzy AHP based decision support system for disaster center location selection and a case study for Istanbul”, Disaster Prevention and Management, 20(5), pp. 499-520.
• Tzeng, G.H. ve Huang, J.J.2011, “Multiple Attribute Decision Making: Methods and Applications”, CRC Press, Taylor & Francis Group, A Chapman&Hall.
• Ukkusuri, S. ve Yushimito, W. 2008, “Location routing approach for the humanitarian prepositioning problem” Transportation Research Record, 2089, pp. 18-25.
• TRABZON AFAD 2016, http://www.trabzonafad.gov.tr/admin/files/1-Afet%20sunum2015-20160111-141433.pdf, (29.06.2016).
• Van Wassenhove, L.V. 2006, “Blackett Memorial Lecture Humanitarian aid logistics: supply chain management in high gear”, Journal of the Operational Research Society, 57, pp. 475-489.
• Verma, A. ve Gaukler, V.M. 2015 “Pre-positioning disaster response facilities at safe locations: An evaluation of deterministic and stochastic modeling approaches”, Computers &Operations Research, 62, pp. 197-209.
• Wang, P., Zhu, Z. ve Wang, Y. 2016, “A novel hybrid MCDM model combining the SAW, TOPSIS and GRA methods based on experimental design”, Information Sciences, 345, pp. 27-45.
• Wu, Z., Sun, J., Liang, L. ve Zha, Y. 2011, “Determination of Weights For Ultimate Cross Efficiency Using Shannon Entropy”, Expert Systems with Applications, 38, pp. 5162-5165.
• Zhang, H., Gu, C.L., Gu, L.W. ve Zhang, Y. 2011. “The evaluation of tourism destination competitiveness by TOPSIS& information entropy - A case in the Yangtze River Delta of China”, Tourism Management, 32, pp. 443-451.