Deprem sonrası sağlık kaynakları planlamasına yönelik matematiksel model önerisi: Kırıkkale kent örneği

Yıl 2023, , 1203 - 1216, 07.10.2022

Sema Çiftçi , Ümit Sami Sakallı

https://doi.org/10.17341/gazimmfd.1092469

Cited By: 2

Öz

Depremler can ve mal kaybına neden olan, en sık görülen, etkileri oldukça büyük afet türüdür. Deprem sonrası kaotik koşullar müdahale ve kurtarma sürecini zorlaştırır. Bu sürecin yönetilebilmesinde, eldeki kaynakların (sağlık, barınma, AFAD ekipleri vb.) başarılı bir şekilde enkaz bölgelerine tahsisi gerekmektedir. Depremlerden sonraki ilk saatlerde yaralıların, yaralanma şiddetlerine göre (ağır ve geciktirilebilir yaralı) ve geçen süreyle doğru orantılı olarak hayatta kalma olasılıkları daha fazladır; zaman ilerledikçe bu ihtimal azalır. Dolayısıyla enkazdan çıkarılan yaralılara ilk müdahalelerinin yapılması, ihtiyaç görülenlerin acilen hastaneye sevk edilmesi gerekmektedir. Bu nedenle sağlık kaynakları planlaması depremin yıkıcı etkilerinin azaltılmasında son derece önemlidir. Bu çalışmada deprem sonrası hayatını kaybeden yaralı sayısının en aza indirilmesini amaçlayan 0-1 karışık tam sayılı bir matematiksel model geliştirilmiştir. Model içerisinde enkaz bölgelerine olay yeri triyajını gerçekleştirecek sağlık birimlerinin (doktor, hemşire, ekipman) tahsis edilmesi, olay yeri triyajı görmüş yaralıların hastaneye sevk işlemi için ambulans ataması yapılması ve ambulansların hangi hastanelere yönlendirilmesi gerektiği kararları verilmek istenmektedir. Geliştirilen modelin çok periyodlu ve yaralıların sağlık durumlarındaki değişimin markov zinciri olarak modellemesi literatüre katkı olarak öne çıkmaktadır. Geliştirilen model, Kırıkkale ili için olası bir deprem senaryosu verileriyle GAMS CPLEX çözücüsü kullanılarak test edilmiştir. Test sonuçları, geliştirilen modelin sağlık kaynakları tahsisi için etkin bir planlama aracı olarak kullanılabileceğini göstermektedir.

Anahtar Kelimeler

Deprem, kaynak tahsisi, optimizasyon, matematiksel programlama, karışık tam sayılı programlama

Kaynakça

• N. Altay and W. G. Green, “OR/MS research in disaster operations management,” European Journal of Operational Research, vol. 175, no. 1, pp. 475–493, 2006, doi: 10.1016/j.ejor.2005.05.016.
• P. Xiong et al., “Towards advancing the earthquake forecasting by machine learning of satellite data,” Science of The Total Environment, vol. 771, p. 145256, Jun. 2021, doi: 10.1016/J.SCITOTENV.2021.145256.
• I. M. Shaluf, “Disaster types,” Disaster Prevention and Management: An International Journal, vol. 16, no. 5, pp. 704–717, 2007, doi: 10.1108/09653560710837019.
• Yildiz, R. Teeuw, J. Dickinson, and J. Roberts, “Children’s earthquake preparedness and risk perception: A comparative study of two cities in Turkey, using a modified PRISM approach,” International Journal of Disaster Risk Reduction, vol. 49, p. 101666, Oct. 2020, doi: 10.1016/j.ijdrr.2020.101666.
• Kaveh, S. M. Javadi, and R. M. Moghanni, “Emergency management systems after disastrous earthquakes using optimization methods: A comprehensive review,” Advances in Engineering Software, vol. 149. Elsevier Ltd, p. 102885, Nov. 01, 2020. doi: 10.1016/j.advengsoft.2020.102885.
• S. French and J. Geldermann, “The varied contexts of environmental decision problems and their implications for decision support,” Environmental Science and Policy, vol. 8, no. 4, pp. 378–391, Aug. 2005, doi: 10.1016/j.envsci.2005.04.008.
• L. R. Thomas, A. S.; Kopczak, “From logistics to supply chain management: the path forward for the humanitarian sector | ALNAP,” Fritz Institute, 2015. https://www.alnap.org/help-library/from-logistics-to-supply-chain-management-the-path-forward-for-the-humanitarian-sector (accessed Jun. 05, 2021).
• Cozzolino, “Humanitarian Logistics and Supply Chain Management,” Springer, Berlin, Heidelberg, 2012, pp. 5–16. doi: 10.1007/978-3-642-30186-5_2.
• Y. Yang and J. Yin, “Multi-coverage Optimal Location Model for Emergency Medical Services (EMS) facilities under various disaster scenarios: A case study of urban fluvial floods in the Minhang District of Shanghai, China,” Natural Hazards and Earth System Sciences Discussions, pp. 1–17, 2019, doi: 10.5194/NHESS-2019-214.
• Y. Shi, G. Zhai, L. Xu, Q. Zhu, and J. Deng, “Planning Emergency Shelters for Urban Disasters: A Multi-Level Location–Allocation Modeling Approach,” Sustainability 2019, Vol. 11, Page 4285, vol. 11, no. 16, p. 4285, Aug. 2019, doi: 10.3390/SU11164285.
• T. D. P. Patra and J. K. Jha, “A two-period newsvendor model for prepositioning with a post-disaster replenishment using Bayesian demand update,” Socio-Economic Planning Sciences, vol. 78, p. 101080, Dec. 2021, doi: 10.1016/J.SEPS.2021.101080.
• K. Nikolopoulos, F. Petropoulos, V. S. Rodrigues, S. Pettit, and A. Beresford, “A disaster response model driven by spatial–temporal forecasts,” International Journal of Forecasting, Feb. 2020, doi: 10.1016/J.IJFORECAST.2020.01.002.
• M. N. Jat and R. A. Rafique, “Mass-Casualty Distribution for Emergency Healthcare: A Simulation Analysis,” International Journal of Disaster Risk Science, vol. 11, no. 3, pp. 364–377, Jun. 2020, doi: 10.1007/S13753-020-00260-3/TABLES/5.
• K. Shin and T. Lee, “Emergency medical service resource allocation in a mass casualty incident by integrating patient prioritization and hospital selection problems,”, vol. 52, no. 10, pp. 1141–1155, Oct. 2020, doi: 10.1080/24725854.2020.1727069.
• R. Sakiani, A. Seifi, and R. R. Khorshiddoust, “Inventory routing and dynamic redistribution of relief goods in post-disaster operations,” Computers & Industrial Engineering, vol. 140, p. 106219, Feb. 2020, doi: 10.1016/J.CIE.2019.106219.
• W. Chen, G. Zhai, C. Ren, Y. Shi, and J. Zhang, “Urban Resources Selection and Allocation for Emergency Shelters: In a Multi-Hazard Environment,” International Journal of Environmental Research and Public Health, vol. 15, no. 6, Jun. 2018, doi: 10.3390/IJERPH15061261.
• M. A. Berawi et al., “Optimizing search and rescue personnel allocation in disaster emergency response using fuzzy logic,” International Journal of Technology, vol. 10, no. 7, pp. 1416–1426, 2019, doi: 10.14716/IJTECH.V10I7.3709.
• S. Zhang, H. Guo, K. Zhu, S. Yu, and J. Li, “Multistage assignment optimization for emergency rescue teams in the disaster chain,” Knowledge-Based Systems, vol. 137, pp. 123–137, Dec. 2017, doi: 10.1016/J.KNOSYS.2017.09.024.
• S. Yousefian, S. Sohrabizadeh, and K. Jahangiri, “Identifying the components affecting intra-organizational collaboration of health sector in disasters: Providing a conceptual framework using a systematic review,” International Journal of Disaster Risk Reduction, vol. 57. Elsevier Ltd, p. 102146, Apr. 15, 2021. doi: 10.1016/j.ijdrr.2021.102146.
• M. Najafi, K. Eshghi, and S. de Leeuw, “A dynamic dispatching and routing model to plan/ re-plan logistics activities in response to an earthquake,” OR Spectrum, vol. 36, no. 2, pp. 323–356, Mar. 2014, doi: 10.1007/s00291-012-0317-0.
• J. B. Coles, J. Zhuang, and J. Yates, “Case study in disaster relief: A descriptive analysis of agency partnerships in the aftermath of the January 12th, 2010 Haitian earthquake,” Socio-Economic Planning Sciences, vol. 46, no. 1, pp. 67–77, Mar. 2012, doi: 10.1016/J.SEPS.2011.08.002.
• W. Yi and A. Kumar, “Ant colony optimization for disaster relief operations,” Transportation Research Part E: Logistics and Transportation Review, vol. 43, no. 6, pp. 660–672, Nov. 2007, doi: 10.1016/J.TRE.2006.05.004.
• Q. Gong and R. Batta, “Allocation and reallocation of ambulances to casualty clusters in a disaster relief operation,”, vol. 39, no. 1, pp. 27–39, Jan. 2011, doi: 10.1080/07408170600743938.
• F. Fiedrich, F. Gehbauer, and U. Rickers, “Optimized resource allocation for emergency response after earthquake disasters,” Safety Science, vol. 35, no. 1–3, pp. 41–57, Jun. 2000, doi: 10.1016/S0925-7535(00)00021-7.
• W. Yi and L. Özdamar, “A dynamic logistics coordination model for evacuation and support in disaster response activities,” European Journal of Operational Research, vol. 179, no. 3, pp. 1177–1193, Jun. 2007, doi: 10.1016/J.EJOR.2005.03.077.
• Jotshi, Q. Gong, and R. Batta, “Dispatching and routing of emergency vehicles in disaster mitigation using data fusion,” Socio-Economic Planning Sciences, vol. 43, no. 1, pp. 1–24, Mar. 2009, doi: 10.1016/J.SEPS.2008.02.005.
• Cotta, “Effective patient prioritization in mass casualty incidents using hyperheuristics and the pilot method,” OR Spectrum, vol. 33, no. 3, pp. 699–720, Jul. 2011, doi: 10.1007/S00291-011-0238-3.
• T. Wilson, G. I. Hawe, G. Coates, and R. S. Crouch, “A multi-objective combinatorial model of casualty processing in major incident response,” European Journal of Operational Research, vol. 230, no. 3, pp. 643–655, Nov. 2013, doi: 10.1016/J.EJOR.2013.04.040.
• M. D. Dean and S. K. Nair, “Mass-casualty triage: Distribution of victims to multiple hospitals using the SAVE model,” European Journal of Operational Research, vol. 238, no. 1, pp. 363–373, Oct. 2014, doi: 10.1016/J.EJOR.2014.03.028.
• M. Najafi, K. Eshghi, and S. de Leeuw, “A dynamic dispatching and routing model to plan/ re-plan logistics activities in response to an earthquake,” OR Spectrum, vol. 36, no. 2, pp. 323–356, Mar. 2014, doi: 10.1007/S00291-012-0317-0/TABLES/17.
• S. Jin, S. Jeong, J. Kim, and K. Kim, “A logistics model for the transport of disaster victims with various injuries and survival probabilities,” Annals of Operations Research, vol. 230, no. 1, pp. 17–33, Jul. 2015, doi: 10.1007/S10479-013-1515-0.
• Sung and T. Lee, “Optimal allocation of emergency medical resources in a mass casualty incident: Patient prioritization by column generation,” European Journal of Operational Research, vol. 252, no. 2, pp. 623–634, Jul. 2016, doi: 10.1016/J.EJOR.2016.01.028.
• Y. Xiang and J. Zhuang, “A medical resource allocation model for serving emergency victims with deteriorating health conditions,” Annals of Operations Research, vol. 236, no. 1, pp. 177–196, Jan. 2016, doi: 10.1007/S10479-014-1716-1/FIGURES/9. D. T. Wilson, G. I. Hawe, G. Coates, and R. S. Crouch, “Online optimization of casualty processing in major incident response: An experimental analysis,” European Journal of Operational Research, vol. 252, no. 1, pp. 334–348, Jul. 2016, doi: 10.1016/J.EJOR.2016.01.021.
• S. Rezapour, N. Naderi, N. Morshedlou, and S. Rezapourbehnagh, “Optimal deployment of emergency resources in sudden onset disasters,” International Journal of Production Economics, vol. 204, pp. 365–382, Oct. 2018, doi: 10.1016/J.IJPE.2018.08.014.
• J. Zhang, H. Liu, G. Yu, J. Ruan, and F. T. S. Chan, “A three-stage and multi-objective stochastic programming model to improve the sustainable rescue ability by considering secondary disasters in emergency logistics,” Computers & Industrial Engineering, vol. 135, pp. 1145–1154, Sep. 2019, doi: 10.1016/J.CIE.2019.02.003.
• K. Liu, “Post-earthquake medical evacuation system design based on hierarchical multi-objective optimization model: An earthquake case study,” International Journal of Disaster Risk Reduction, vol. 51, p. 101785, Dec. 2020, doi: 10.1016/J.IJDRR.2020.101785.
• H. Sun, Y. Wang, J. Zhang, and W. Cao, “A robust optimization model for location-transportation problem of disaster casualties with triage and uncertainty,” Expert Systems with Applications, vol. 175, p. 114867, Aug. 2021, doi: 10.1016/J.ESWA.2021.114867.
• H. Sun, Y. Wang, and Y. Xue, “A bi-objective robust optimization model for disaster response planning under uncertainties,” Computers and Industrial Engineering, vol. 155, May 2021, doi: 10.1016/J.CIE.2021.107213.
• G. Galindo and R. Batta, “Review of recent developments in OR/MS research in disaster operations management,” European Journal of Operational Research, vol. 230, no. 2, pp. 201–211, Oct. 2013, doi: 10.1016/J.EJOR.2013.01.039.
• Y. Liu, N. Cui, and J. Zhang, “Integrated temporary facility location and casualty allocation planning for post-disaster humanitarian medical service,” Transportation Research Part E: Logistics and Transportation Review, vol. 128, pp. 1–16, Aug. 2019, doi: 10.1016/J.TRE.2019.05.008.
• S. Çiftçi, Z. Çakırer, and Ü. S. Sakallı, “Simülasyon Çalışması ile Deprem Senaryosu Oluşturulması ve Kırıkkale İli Kayıp Tahmin Analizi,” Uluslararası Mühendislik Araştırma ve Geliştirme Dergisi, vol. 12, no. 2, pp. 603–617, Jun. 2020, doi: 10.29137/UMAGD.705067.
• S. O. Akbaş , Y. B. Sönmezer and N. S. Işık“Assessment Of The Peak Acceleration, Amplification Ratio And Fundamental Period Properties For The Kirikkale Province Settlement Area,” Journal of the Faculty of Engineering and Architecture of Gazi University, vol. 30, no. 4, pp. 711–721, Dec. 2015, doi: 10.17341/GUMMFD.54639.
• F. Mills, N. T. Argon, and S. Ziya, “Resource-Based Patient Prioritization in Mass-Casualty Incidents,” http://dx.doi.org/10.1287/msom.1120.0426, vol. 15, no. 3, pp. 361–377, Jan. 2013, doi: 10.1287/MSOM.1120.0426.
• W. J. Sacco, D. M. Navin, R. K. Waddell, K. E. Fiedler, W. B. Long, and R. F. Buckman, “A new resource-constrained triage method applied to victims of penetrating injury,” The Journal of trauma, vol. 63, no. 2, pp. 316–325, Aug. 2007, doi: 10.1097/TA.0B013E31806BF212.
• D. Widgery, “Health Statistics,” 2019. doi: 10.1080/09505438809526230.