Post-Earthquake Casualty Transport Optimization

Disasters are one of the deadliest problems faced by humans in their struggle to survive in nature throughout history. This study was carried out for the optimization of the casualty transport process after the earthquake, which is one of the most powerful disaster types today. This study, in which what needs to be done in the post-earthquake response phase is planned, it is aimed to design a logistics network that will ensure that the patients who survive the earthquake with severe injuries are treated as soon as possible. For the problem’s resolution in this way, a two-stage solution model is recommended. The proposed mathematical model is first solved using a mixed integer programming approach. The outputs of the mathematical model were simulated using the new patient selection procedure in the second stage. Thanks to the proposed model, it has been determined which ambulances to transport the patients to which hospital and preferences that will minimize the total time have been formed. Finally, the developed new mathematical model and patient selection method were tested with earthquake forecast data for the Tuzla District of Istanbul Province. After conducting a baseline study, the model’s sensitivity to changes in parameters and constraints under various scenarios was assessed (scenario 2, scenario 3). Sensitivity analysis results showed improvements in total transport times with additional hospital setup and ambulance allocation.

Keywords:

Casualty Transport, Disaster Logistics, Disaster Management, Earthquake Response, Logistics Network Design Network Optimization, Mixed Integer Programming,

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Adıvar, B., & Mert, A. (2010). International disaster relief planning with fuzzy credibility. Fuzzy Optimization and Decision Making, 9(4), 413–433. https://doi.org/10.1007/s10700-010-9088-8
Adrang, H., Amiri, A. B., Damghani, K. K., & Moghaddam, R. T. (2020). Planning for Medical Emergency Transportation Vehicles during Natural Disasters. Journal of Optimization in Industrial Engineering, 0(2), 185–197. https://doi.org/10.22094/joie.2020.688.1455
AFAD. (2020). Açıklamalı Afet Yönetimi Terimleri Sözlüğü. https://www.afad.gov.tr/aciklamali-afet-yonetimi- terimleri-sozlugu
Altay, N. (2008). Issues in disaster relief logistics. Large-Scale Disasters: Prediction, Control, and Mitigation,
9780521872, 120–146. https://doi.org/10.1017/CBO9780511535963.007
Altay, N., & Green, W. G. (2006). OR/MS research in disaster operations management. European Journal of Operational Research, 175(1), 475–493. https://doi.org/10.1016/j.ejor.2005.05.016
Anaya-Arenas, A. M., Renaud, J., & Ruiz, A. (2014). Relief distribution networks: a systematic review.
Annals of Operations Research 2014 223:1, 223(1), 53–79. https://doi.org/10.1007/S10479-014-1581-Y
Balcik, B., Beamon, B. M., & Smilowitz, K. (2008). Last mile distribution in humanitarian relief. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 12(2), 51–63. https://doi. org/10.1080/15472450802023329
Barbarosoǧlu, G., & Arda, Y. (2004). A two-stage stochastic programming framework for transportation planning in disaster response. Journal of the Operational Research Society. https://doi.org/10.1057/palgrave.jors.2601652
Barbarosolu, G., Özdamar, L., & Çevik, A. (2002). An interactive approach for hierarchical analysis of helicopter logistics in disaster relief operations. European Journal of Operational Research, 140(1), 118–133. https://doi.org/10.1016/S0377-2217(01)00222-3
Berkoune, D., Renaud, J., Rekik, M., & Ruiz, A. (2012). Transportation in disaster response operations. Socio-Economic Planning Sciences, 46(1), 23–32. https://doi.org/10.1016/j.seps.2011.05.002
Boeing, G. (2017). OSMnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks. Computers, Environment and Urban Systems, 65, 126–139. https://doi.org/10.1016/j. compenvurbsys.2017.05.004
Caunhye, A. M., Nie, X., & Pokharel, S. (2012). Optimization models in emergency logistics: A literature review. Socio-Economic Planning Sciences, 46(1), 4–13. https://doi.org/10.1016/J.SEPS.2011.04.004
Chern, C. C., Chen, Y. L., & Kung, L. C. (2010). A heuristic relief transportation planning algorithm for emergency supply chain management. International Journal of Computer Mathematics, 87(7), 1638– 1664. https://doi.org/10.1080/00207160802441256
Deniz, E. Ş. (2012). Antalya İli Afet Riskleri Ve Afet Yönetimi Konusu Üzerine Bir Araştırma. Akdeniz Üniversitesi.
Ergünay, O. (2005). Afet Yönetiminde İşbirliği ve Koordinasyonun Önemi. TC İçişleri Bakanlığı-JICA, 9–18.
Gu, Y. (2011). Research On Optimization Of Relief Supplies Distribution Aimed To Minimize Disaster
Losses. Journal of Computers, 6(3), 603–609. https://doi.org/10.4304/JCP.6.3.603-609
Gümüşbuğa, F. (2012). Afet Yönetimi Kapsamında Hata Ağacı Analizi İle Risk Tabanlı Tesis Yer Seçimi. Kara Harp Okulu.
Haghani, A., & Oh, S. C. (1996). Formulation and solution of a multi-commodity, multi-modal network flow model for disaster relief operations. Transportation Research Part A: Policy and Practice, 30(3), 231–250. https://doi.org/10.1016/0965-8564(95)00020-8
Hu, Z. H. (2011). A container multimodal transportation scheduling approach based on immune affinity model for emergency relief. Expert Systems with Applications, 38(3), 2632–2639. https://doi.org/10.1016/J. ESWA.2010.08.053
Huang, H., Li, Y., Huang, B., Pi, X., Xiangru, M., Wei, S., Nickel, S., Saldanha-Da-Gama, F., Kim, J. S., Lee, D.-H. D. H. D. H., Ge, X. Q., Tian, Z. Y., Zhang, J., Thanh, P. N., Bostel, N., Péton, O., Alshamsi, A., Diabat, A., Jeet, V., Chen, S. S. (2009). Network design based on closed-loop reverse logistics recycling. International Journal of Production Research, 50(1), 533–536. https://doi.org/10.1109/CASE.2009.11
IFRC. (2020). What is a disaster? https://www.ifrc.org/en/what-we-do/disaster-management/about-disasters/ what-is-a-disaster/ Jotshi, A., Gong, Q., & Batta, R. (2009). Dispatching and routing of emergency vehicles in disaster mitigation using data fusion. Socio-Economic Planning Sciences, 43(1), 1–24. https://doi.org/10.1016/j. seps.2008.02.005
Kadıoğlu, M. (2011). Afet Yönetimi Beklenilmeyeni Beklemek, En Kötüsünü Yönetmek. T.C. Marmara Belediyeler Birliği Yayını, 40–43.
Kaya, Y. (2000). 17 Ağustos’un Ardından Deprem, Devlet ve Toplum. Otopsi Yayınevi Yayınları.
Knott, R. (1987). The logistics of bulk relief supplies. Disasters, 11(2), 113–115. https://doi. org/10.1111/J.1467-7717.1987.TB00624.X
Konstantinidou, M. A., Kepaptsoglou, K. L., & Stathopoulos, A. (2019). A multi-objective network design model for post-disaster transportation network management. Promet - Traffic - Traffico, 31(1), 11–23. https://doi.org/10.7307/ptt.v31i1.2743
Öner, Z. S. (2010). Türkiye’de Afet Yönetimi ve Niğde Örneği. Niğde Üniversitesi.
Ozdamar, L. (2011). Planning helicopter logistics in disaster relief. OR Spectrum 2011 33:3, 33(3), 655–672. https://doi.org/10.1007/S00291-011-0259-Y
Özdamar, L., Ekinci, E., & Küçükyazici, B. (2004). Emergency logistics planning in natural disasters. Annals of Operations Research. https://doi.org/10.1023/B:ANOR.0000030690.27939.39
Safeer, M., Anbuudayasankar, S. P., Balkumar, K., & Ganesh, K. (2014). Analyzing transportation and distribution in emergency humanitarian logistics. Procedia Engineering, 97, 2248–2258. https://doi. org/10.1016/j.proeng.2014.12.469
Sarma, D., Singh, A., Das, A., & Bera, U. K. (2018). A Post-Disaster Humanitarian Relief Logistic Model: Evacuation and Transportation. 2018 3rd International Conference for Convergence in Technology, I2CT 2018, 1–5. https://doi.org/10.1109/I2CT.2018.8529786
Sarp, N. (1999). Disaster Management in Healthcare.
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 Review, 43(6), 687–709. https://doi.org/10.1016/j.tre.2006.04.004
Sheu, J. B. (2010). Dynamic relief-demand management for emergency logistics operations under large-scale disasters. Transportation Research Part E: Logistics and Transportation Review, 46(1), 1–17. https:// doi.org/10.1016/j.tre.2009.07.005
Tanrıöven, E. A. (2010). A Study of Ambulance Dispatching Policies To Improve Disaster Relief Operations : A Case Study on Istanbul. Koç University.
Tzeng, G. H., Cheng, H. J., & Huang, T. D. (2007). Multi-objective optimal planning for designing relief delivery systems. Transportation Research Part E: Logistics and Transportation Review, 43(6), 673–686. https://doi.org/10.1016/j.tre.2006.10.012
Vitoriano, B., Ortuño, T., & Tirado, G. (2009). HADS, a goal programming-based humanitarian aid distribution system. Journal of Multi-Criteria Decision Analysis, 16(1–2), 55–64. https://doi.org/10.1002/mcda.439
Vitoriano, Begoña, Ortuño, M. T., Tirado, G., & Montero, J. (2011). A multi-criteria optimization model for humanitarian aid distribution. Journal of Global Optimization, 51(2), 189–208. https://doi.org/10.1007/ s10898-010-9603-z
Wang, S. Ma, Z. and Li, Z. (2008). A Dynamic Programming Model for Optimal Transportation of Emergency Relief Commodities in Natural Disasters. 0, 1452–1457.
Yi, W., & Kumar, A. (2007). Ant colony optimization for disaster relief operations. Transportation Research
Part E: Logistics and Transportation Review, 43(6), 660–672. https://doi.org/10.1016/j.tre.2006.05.004
Yi, W., & Özdamar, L. (2004). Fuzzy Modeling for Coordinating Logistics in Emergencies.
Yi, W., & Özdamar, L. (2007). A dynamic logistics coordination model for evacuation and support in disaster response activities. European Journal of Operational Research, 179(3), 1177–1193. https:// doi.org/10.1016/j.ejor.2005.03.077
Zhu, L., Gong, Y., Xu, Y., & Gu, J. (2019). Emergency relief routing models for injured victims considering equity and priority. Annals of Operations Research, 283(1–2), 1573–1606. https://doi.org/10.1007/ s10479-018-3089-3
Zuo, X. D., Qing, Y. H., & Li, J. P. (2014). Research on Design of Region Postal Logistic Distribution Network with Time Windows. Strategy in Emerging Markets: Management, Finance and Sustainable Development, 205–211.