AFET LOJİSTİĞİ RİSKLERİNİN LİTERATÜR ARAŞTIRMASINA DAYALI OLARAK BELİRLENMESİ

Year 2019, Volume: 6 Issue: 1, 1 - 9, 30.03.2019

Aylin Ofluoglu Birdogan Baki , İlker Murat Ar

https://doi.org/10.17261/Pressacademia.2019.1029

Abstract

Amaç - Afet lojistiği risklerinin belirlenmesi ve analiz edilmesi, kurumlara afet lojistiği kapsamında oluşturacakları planlar ve alacakları önlemler açısından yol gösterici olmaktadır. Bu çalışmanın temel amacı, afet lojistiği kapsamındaki risk unsurlarının ve bu risklerin azaltılmasına yönelik farklı yöntemlerin literatür araştırması ile ortaya konmasıdır.

Yöntem - Ocak 2011 - Aralık 2018 arasında afet lojistiği riskleri üzerine yapılmış çalışmalar incelenmiştir. Literatür araştırması; EmeraldInsight, ScienceDirect, Scopus, Taylor&Francis Online ve SpringerLink veri tabanları üzerinden “disaster logistics risk”, “humanitarian logistics risk” ve “emergency logistics risk” anahtar kelimeleri kullanılarak gerçekleştirilmiştir

Bulgular - Araştırma sonucunda literatürde en fazla dikkate alınan afet lojistiği riskinin “talep riski” olduğu tespit edilmiştir. Sonuçlara gore afet lojistiği kapsamında en fazla ele alınan konunun tesis kuruluş yeri seçim kararı ve belirsizlik altında en fazla kullanılan çözüm yönteminin de Stokastik Programlama olduğu ortaya konmuştur.

Sonuç- Afet lojistiğ kapsamında oluşturulacak planlar ve alınacak önlemler açısından risklerin düşünülmesi oldukça önemlidir. Gelecek çalışmalarda daha fazla veri tabanı eklenerek çalışmanın kapsamı genişletilebilir. Afet lojistiği risklerini azaltacak stratejilerin geliştirilmesine dair araştırmalar gerçekleştirilebilir.

Keywords

Afet lojistiği, afet lojistiği riskleri, literatür araştırması

DETERMINING OF DISASTER LOGISTICS RISKS BASED ON LITERATURE REVIEW

Abstract

Purpose - Identifying and analysing of disaster logistics risks is a guide to the organizations according to the disaster logistics plans and actions to be taken. The main pupose of this study is to reveal the disaster logistics risks and the used methods to mitigate the effects of the risks.

Methodology - The researches on disaster logistics risks were reviewed between January 2011-December 2018. The literature review was made on the databases EmeraldInsight, ScienceDirect, Scopus, Taylor&Francis Online and SpringerLink by using the keywords “disaster logistics risk”, “humanitarian logistics risk” and “emergency logistics risk”.

Findings - The survey results show that demand risk is the most considered disaster logistics risk. According to the results, facility location decisions is the most addressed disaster logistics issue and Stochastic Programming is the most used solution method under risk based uncertainty.

Conclusion - Considering the risks is very important for the plans and actions to be taken in context of disaster logistics. For future studies, the scope of the study can be extended by adding more databases. Studies can be made to develop strategic decisions to mitigate the effects of the disaster logistics risks.

Keywords

Disaster logistics, disaster logistics risks, literature review

References

• AFAD (2014). Açıklamalı afet yönetimi terimleri sözlüğü. https://www.afad.gov.tr/upload/Node/3495/xfiles/sozluk.pdf, (27.11.2017).
• Afshar, A., Haghani, A. (2012). Modeling integrated supply chain logistics in real-time large-scale disaster relief operations. Socio-Economic Planning Sciences, 46, pp.327-338.
• Ahmadi, M., Seifi, A., 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.
• Akgün, A., Gümüşbuğa, F., Tansel, B. (2015). Risk based facility location by using fault tree analysis in disaster management. Omega, 52, pp.168–179.
• Alem, D., Clark, A., Moreno, A. (2016). Stochastic network models for logistics planning in disaster relief. European Journal of Operational Research, 255, pp.187–206.
• Babaei, A., Shahanaghi, K. (2018). A novel algorithm for identifying and analyzing humanitarian relief logistics problems: Studying uncertainty on the basis of interaction with the decision maker. Process Integration and Optimization for Sustainability, 2, pp.27–45.
• Baharmand, H., Comes, T., Lauras, M. (2017). Managing in-country transportation risks in humanitarian supply chains by logistics service providers: Insights from the 2015 Nepal earthquake. International Journal of Disaster Risk Reduction, 24, pp.549–559.
• Barzinpour, F., Esmaeili, V. (2014). A multi-objective relief chain location distribution model for urban disaster management. International Journal of Advanced Manufacturing Technology, 70, pp.1291–1302.
• Bastian, N. D., Griffin, P. M., Spero, E., Fulton, L. V. (2016). Multi-criteria logistics modeling for military humanitarian assistance and disaster relief aerial delivery operations. Optimization Letters, 10, pp.921–953.
• Ben-Tal, A., Chung, B. D., Mandala, S. R., Yao, T. (2011). Robust optimization for emergency logistics planning: Risk mitigation in humanitarian relief supply chains. Transportation Research Part B, 45, pp.1177–1189.
• Bozorgi-Amiri, B., Jabalameli, M. S., Alinaghian, M., Heydari, M. (2012). A modified particle swarm optimization for disaster relief logistics under uncertain environment. International Journal of Advanced Manufacturing Technology, 60, pp.357–371.
• Bozorgi-Amiri, B., Jabalameli, M. S., Mirzapour Al-e-Hashem, S. M. J. (2013). A multi-objective robust stochastic programming model for disaster relief logistics under uncertainty. OR Spectrum, 35, pp.905–933.
• Caunhye, A. M., Zhang, Y., Li, M., Nie, X. (2016). A location-routing model for prepositioning and distributing emergency supplies. Transportation Research Part E, 90, pp.161–176.
• Celik, E., Gumus, A. T. (2016). An outranking approach based on interval type-2 fuzzy sets to evaluate preparedness and response ability of non-governmental humanitarian relief organizations. Computers & Industrial Engineering, 101, pp.21–34.
• Celik, E., Gumus, A. T., Alegoz, M. (2014). A trapezoidal type-2 fuzzy MCDM method to identify and evaluate critical success factors for humanitarian relief logistics management. Journal of Intelligent & Fuzzy Systems, 27, pp.2847–2855.
• Chapman, A. G., Mitchell, J. E. (2018). A fair division approach to humanitarian logistics inspired by conditional value-at-risk. Annals of Operations Research, 262, pp.133–151.
• Charles, A., Lauras, M., Van Wassenhove, L. N., Dupont, L. (2016). Designing an efficient humanitarian supply network. Journal of Operations Management, 47-48, pp.58-70.
• Chen, W., Feng, Q., Xu, Q. (2010). Emergency logistics risk assessment based on AHM. Proceedings of the International Conference of Information Science and Management Engineering (ISME 2010), pp.59-61.
• Chen, J., Liang, L., Yao, D. Q. (2017a). Pre-positioning of relief inventories for non-profit organizations: a newsvendor approach. Annals of Operations Research, 259, pp.35–63.
• Chen, Y. X., Tadikamalla, P. R., Shang, J., Song, Y. (2017b). Supply allocation: bi-level programming and differential evolution algorithm for Natural Disaster Relief. Cluster Computing, pp.1-15.
• Cheng, Q., Yu, L. (2010). Early warning index system for natural disasters emergency logistics risks. Proceedings of the International Conference on Logistics Engineering and Intelligent Transportation Systems (LEITS2010), pp.173-176.
• Condeixa, L. D., Leiras, A., Oliveira, F., De Brito Jr, I. (2017). Disaster relief supply pre-positioning optimization: A risk analysis via shortage mitigation. International Journal of Disaster Risk Reduction, 25, pp.238–247.
• Díaz-Delgado, C., Iniestra, J. G. (2014). Flood risk assessment in humanitarian logistics process design. Journal of Applied Research and Technology, 12, pp.976-984.
• Döyen, A., Aras, N., Barbarasoğlu, G. (2012). A two-echelon stochastic facility location model for humanitarian relief logistics. Optimization Letters, 6, pp.1123–1145
• Elçi, Ö., Noyan, N. (2018). A chance-constrained two-stage stochastic programming model for humanitarian relief network design. Transportation Research Part B, 108, pp.55-83.
• Fereiduni, M., Shahanaghi, K. (2017). A robust optimization model for distribution and evacuation in the disaster response phase. Journal of Industrial Engineering International, 13, pp.117–141.
• He, Y., Liang, X. D., Deng, F. M., Li, Z. (2018). Emergency supply chain management based on rough set – house of quality. International Journal of Automation and Computing, pp.1-13.
• Hu, Z. H., Sheu, J. B. (2013). Post-disaster debris reverse logistics management under psychological cost minimization. Transportation Research Part B, 55, pp.118–141.
• Hu, S. L., Han, C. F., Meng, L. P. (2016). Stochastic optimization for investment in facilities in emergency prevention. Transportation Research Part E, 89, pp.14–31.
• Iakovou, E., Vlachos, D., Keramydas, C., Partsch, D. (2014). Dual sourcing for mitigating humanitarian supply chain disruptions. Journal of Humanitarian Logistics and Supply Chain Management, 4(2), pp.245-264.
• Ivgin, M. (2013). The decision-making models for relief asset management and interaction with disaster mitigation. International Journal of Disaster Risk Reduction, 5, pp.107–116.
• Jahre, M. (2017). Humanitarian supply chain strategies - A review of how actors mitigate supply chain risks. Journal of Humanitarian Logistics and Supply Chain Management, 7(2), pp.82-101.
• Jeong, K. Y., Hong, J. D., Xie, Y. (2014). Design of emergency logistics networks, taking efficiency, risk and robustness into consideration. International Journal of Logistics Research and Applications, 17(1), pp.1-22.
• Jha, A., Acharya, D., Tiwari, M. K. (2017). Humanitarian relief supply chain: a multi-objective model and solution. Sadhana, 42(7), pp. 1167–1174.
• Kabra, G., Ramesh, A., Arshinder, K. (2015). Identification and prioritization of coordination barriers in humanitarian supply chain management. International Journal of Disaster Risk Reduction, 13, pp.128–138.
• Kamyabniya, A., Lotfi, M. M., Naderpour, M., Yih, Y. (2018). Robust platelet logistics planning in disaster relief operations under uncertainty: a coordinated approach. Information Systems Frontiers, 20, pp.759–782.
• Kovacs, G., Spens, K. (2009). Identifying challenges in humanitarian logistics. International Journal of Physical Distribution & Logistics Management, 39(6), pp.506-528.
• Liu, J., Zhou, H., Wang, J. (2018). The coordination mechanisms of emergency inventorymodel under supply disruptions. Soft Computing, 22, pp.5479–5489.
• Malekpoor, H., Chalvatzis, K., Mishra, N., Ramudhin, A. (2018). A hybrid approach of VIKOR and bi-objective integer linear programming for electrification planning in a disaster relief camp. Annals of Operations Research, pp.1-27.
• Mohamadi, A., Yaghoubi, S., Pishvaee, M. S. (2016). Fuzzy multi-objective stochastic programming model for disaster relief logistics considering telecommunication infrastructures: a case study. Operational Research, pp.1-41.
• Molladavoodi, H., Paydar, M. M., Safaei, A. S. (2018). A disaster relief operations management model: a hybrid LP–GA approach. Neural Computing and Applications, pp.1-22.
• Nagurney, A., Nagurney, L. S. (2016). A mean-variance disaster relief supply chain network model for risk reduction with stochastic link costs, time targets, and demand uncertainty. Springer Proceedings in Mathematics and Statistics, 185, pp.231-255.
• Nolz, P. C., Semet, F., Doerner, K. F. (2011). Risk approaches for delivering disaster relief supplies. OR Spectrum, 33, pp.543–569.
• Noyan, N. (2012). Risk-averse two-stage stochastic programming with an application to disaster management. Computers & Operations Research, 39, pp. 541–559.
• Noyan, N., Kahvecioğlu, G. (2018). Stochastic last mile relief network design with resource reallocation. OR Spectrum, 40, pp.187–231.
• Pettit, S., Beresford, A. (2009). Critical success factors in the context of humanitarian aid supply chains. International Journal of Physical Distribution & Logistics Management, 39(6), pp.450-468.
• Rahmani, D. (2018). Designing a robust and dynamic network for the emergency blood supply chain with the risk of disruptions. Annals of Operations Research, pp.1-29.
• Rawls, C. G., Turnquist, M. A. (2011). Pre-positioning planning for emergency response with service quality constraints. OR Spectrum, 33, pp.481–498.
• Rawls, C. G., Turnquist, M. A. (2012). Pre-positioning and dynamic delivery planning for short-term response following a natural disaster. Socio-Economic Planning Sciences, 46, pp.46-54.
• Rezaei-Malek, M., Tavakkoli-Moghaddam, R., Zahiri, B., Bozorgi-Amiri, A. (2016). An interactive approach for designing a robust disaster relief logistics network with perishable commodities. Computers & Industrial Engineering, 94, pp.201–215.
• Rodríguez, J. T., Vitoriano, B., Montero, J. (2012). A general methodology for data-based rule building and its application to natural disaster management. Computers & Operations Research, 39, pp.863–873.
• Safaei, A. S., Farsad, S., Paydar, M. M. (2018). Emergency logistics planning under supply risk and demand uncertainty. Operational Research, pp.1-24.
• Sahebi, I. G., Arab, A., Moghadam, M. R. S. (2017). Analyzing the barriers to humanitarian supply chain management: A case study of the Tehran Red Crescent Societies. International Journal of Disaster Risk Reduction, 24, pp.232–241.
• Sebatli, A., Cavdur, F., Kose-Kucuk, M. (2017). Determination of relief supplies demands and allocation of temporary disaster response facilities. Transportation Research Procedia, 22, pp.245–254.
• Vahdani, B., Veysmoradi, D., Shekari, N., Mousavi, S. M. (2018). Multi-objective, multi-period location-routing model to distribute relief after earthquake by considering emergency roadway repair. Neural Computing & Applications, 30, pp.835–854.
• Van der Laan, E. A., De Brito, M. P., Van Fenema, P. C., Vermaesen, S. C. (2009). Managing information cycles for intra-organisational coordination of humanitarian logistics. International Journal of Services Technology and Management, 12 (4), pp.362-390.
• Van der Laan, E., Van Dalen, J., Rohrmoser, M., Simpson, R. (2016). Demand forecasting and order planning for humanitarian logistics: An empirical assessment “, Journal of Operations Management, 45, pp.114-122.
• Wang, L., Song, J., Shi, L. (2015). Dynamic emergency logistics planning: models and heuristic algorithm. Optimization Letters, 9, pp.1533-1552.
• Wang, B. C., Li, M., Hu, Y., Huang, L., Lin, S. M. (2018). Optimizing locations and scales of emergency warehouses based on damage scenarios. Journal of the Operations Research Society of China, pp.1-20.
• Yadav, D. K., Barve, A. (2015). Analysis of critical success factors of humanitarian supply chain: An application of Interpretive Structural Modeling. International Journal of Disaster Risk Reduction, 12, pp.213–225.
• Yahyaei, M., Bozorgi-Amiri, A. (2018). Robust reliable humanitarian relief network design: an integration of shelter and supply facility location. Annals of Operations Research, In Press, pp.1-20.
• Yang, F., Yuan, Q., Du, S., Liang, L. (2016). Reserving relief supplies for earthquake: a multi-attribute decision making of China Red Cross. Annals of Operations Research, 247, pp.759–785
• Yang, Z., Guo, L., Yang, Z. (2017). Emergency logistics for wildfire suppression based on forecasted disaster evolution. Annals of Operations Research, pp.1-21.
• Zhao, M., Chen, Q. (2015). Risk-based optimization of emergency rescue facilities locations for large-scale environmental accidents to improve urban public safety. Natural Hazards, 75, pp.163-189.
• Zheng, Y. Z., Ling, H. F. (2013). Emergency transportation planning in disaster relief supply chain management: A cooperative fuzzy optimization approach. Soft Computing, 17, pp.1301–1314.
• Zolfaghari, M. R., Peyghaleh, E. (2015). Implementation of equity in resource allocation for regional earthquake risk mitigation using two-stage stochastic programming. Risk Analysis, 35(3), pp.434-458.