TY  - JOUR
T1  - Afet Lojistiğinde Üç Aşamalı Karma Tamsayılı Bir Model Önerisi
TT  - A Three-Stage Mixed Integer Model Proposal in Disaster Logistics
AU  - Soyöz, Hüseyin
AU  - Özyörük, Bahar
PY  - 2021
DA  - June
Y2  - 2021
JF  - Journal of Turkish Operations Management
JO  - JTOM
PB  - METE GÜNDOĞAN
WT  - DergiPark
SN  - 2630-6433
SP  - 641
EP  - 661
VL  - 5
IS  - 1
LA  - tr
AB  - Dünyada ve Türkiye’de afet nedeniyle hayatını kaybeden insanların sayısı günden güne artmaktadır. Ölen insanların bir kısmı afet esnasında bir kısmı ise afet sonrasındaki yetersizlikler sebebiyle hayatını kaybetmektedir.  Bu sebeple afet öncesinde yapılan ve sonrasında yapılacak işlemlerin önceden planlanması önemli bir konu haline gelmiştir. Bu konu ile ilgili olarak araştırmacılar son yıllarda afet lojistiği üzerinde durmaktadırlar. Afet lojistiğinin aşamaları afet öncesi hazırlık, afet müdahale süreci ve müdahale sonrası lojistik faaliyetler şeklindedir. Bu çalışmada afet sonrasında kriz alanındaki insanların barınaklara ve sağlık merkezlerine taşınması, aynı zamanda tedarikçilerden depolar vasıtasıyla barınaklarda bulunan insanlara zaruri ihtiyaçlarını sağlık merkezinde bulunan insanlara ise zaruri ihtiyaçlarını ve sağlık malzemelerini taşınmasını sağlamak üzere üç aşamalı karma tamsayılı bir matematiksel model önerilmiştir. Bu matematiksel model küçük bir veri seti ile test edilerek sonuçları paylaşılmıştır.
KW  - Afet lojistiği
KW  - İnsani yardım lojistiği
KW  - karma tam sayılı programlama
N2  - The number of people who died because of disasters in the world and Turkey is increasing day by day. Some of the people who died die during the disaster, and some of them die due to inadequacies after the disaster.  For this reason, it has become an important issue to plan the actions before and after the disaster Regarding this issue, researchers have focused on disaster logistics in recent years. The stages of disaster logistics are pre-disaster preparedness, disaster response process and post-response logistics activities.  In this study, a 3-stage mixed integer mathematical model is proposed to transport people in crisis areas to shelters and health centers after disasters, as well as to provide the necessary needs and health supplies to people through warehouses from suppliers. This mathematical model was tested with a small data set and the results were shared.
CR  - Ablanedo-Rosas, J. H., Gao, H., Alidaee, B. & Teng, W. Y. (2009). Allocation of emergency and recovery centres in Hidalgo, Mexico. International Journal of Services Sciences, 2(2), 206-218. doi: https://doi.org/10.1504/IJSSCI.2009.024941
CR  - Babaei A. & Shahanaghi, K.. (2017). A new model for planning the distributed facilities locations under emergency conditions and uncertainty space in relief logistics. Uncertain Supply Chain Management, 5, 105–125. doi: https://doi.org/10.5267/j.uscm.2016.10.004
CR  - Barbarosoǧlu, G. & Arda, Y. (2004). A two-stage stochastic programming framework for transportation planning in disaster response. Journal of the Operational Research Society, 55, 43–53. doi: https://doi.org/10.1057/palgrave.jors.2601652
CR  - Campbell, A. M. & Jones, P. C. (2011). Prepositioning supplies in preparation for disasters. European Journal of Operational Research, 209(2), 156-165. doi: https://doi.org/10.1016/j.ejor.2010.08.029
CR  - Cao, C., Liu, Y., Tang, O. & Gao, X. (2021). A fuzzy bi-level optimization model for multi-period post-disaster relief distribution in sustainable humanitarian supply chains. International Journal of Production Economics 235(1). doi: https://doi.org/10.1016/j.ijpe.2021.108081
CR  - Döyen, A., Aras, N. & Barbarosoğlu, G. (2012). A two-echelon stochastic facility location model for humanitarian relief logistics. Optimization Letters, 6(6), 1123-1145. doi: https://doi.org/10.1007/s11590-011-0421-0
CR  - Duran, S., Gutierrez, M. A. & Keskinocak, P. (2011). Pre-positioning of emergency items for CARE international. Interfaces, 41(3), 223-237. doi: https://doi.org/10.2307/23016301
CR  - Galindo, G. & Batta, R. (2013). Prepositioning of supplies in preparation for a hurricane under potential destruction of prepositioned supplies. Socio-Economic Planning Sciences, 47(1), 20-37. doi: https://doi.org/10.1016/j.seps.2012.11.002
CR  - Garrido, R. A., Lamas, P. & Pino, F. J. (2015). A stochastic programming approach for floods emergency logistics. Transportation research part E: logistics and transportation review, 75, 18-31. doi: https://doi.org/10.1016/j.tre.2014.12.002
CR  - Görmez, N., Köksalan, M. & Salman, F. S. (2011). Locating disaster response facilities in Istanbul. Journal of the Operational Research Society, 62(7), 1239-1252. doi: https://doi.org/10.1057/jors.2010.67
CR  - Günneç, D. & Salman, F. S. (2007). A two-stage multi-criteria stochastic programming model for location of emergency response and distribution centers. International Network Optimization Conference (INOC), Spa, Belgium, 22-25.
CR  - Günneç, D. & Salman, F. S. (2011). Assessing the reliability and the expected performance of a network under disaster risk. OR Spectrum, 33(3), 499-523. doi: https://doi.org/10.1007/s00291-011-0250-7
CR  - Hasani, A. & Mokhtarib, H. (2019). An integrated relief network design model under uncertainty:A case of Iran. Safety Science, 111, 22–36. doi: https://doi.org/10.1016/j.ssci.2018.09.004
CR  - Hong, X., Lejeune, M. A. & Noyan N. (2015). Stochastic network design for disaster preparedness. IIE Transactions, 47, 329-357. doi: https://doi.org/10.1080/0740817X.2014.919044
CR  - Huang, R., Kim, S. & Menezes, M. B. (2010). Facility location for large-scale emergencies. Annals of Operations Research, 181(1), 271-286. doi: https://doi.org/10.1007/s10479-010-0736-8
CR  - Hu, S. L., Han, C. F. & Meng, L. P. (2017). Stochastic optimization for joint decision making of inventory and procurement in humanitarian relief. Computers & Industrial Engineering, 111, 39-49. doi: https://doi.org/10.1016/j.cie.2017.06.029
CR  - Jha, A., Acharya, D. & Tiwar, M. K., (2017). Humanitarian relief supply chain: a multi-objective model and solution. Sadhana, 42 (7), 1167–1174. doi:  https://doi.org/10.1007/s12046-017-0679-8
CR  - Lee, Y. M., Ghosh, S. & Ettl, M. (2009). Simulating distribution of emergency relief supplies for disaster response operations. Proceedings of the 2009 Winter Simulation Conference. doi: https://doi.org/10.1109/WSC.2009.5429246
CR  - Li, X., Ramshani, M. & Huang, Y. (2018). Cooperative maximal covering models for humanitarian relief chain management. Computers & Industrial Engineering, 119, 301–308. doi: https://doi.org/10.1016/j.cie.2018.04.004
CR  - Lu, C. C. (2013). Robust weighted vertex p-center model considering uncertain data: An application to emergency management. European Journal of Operational Research, 230(1), 113-121. doi: https://doi.org/10.1016/j.ejor.2013.03.028
CR  - Lu, X. L. & Hou, Y. X. (2009). Ant colony optimization for facility location for large-scale emergencies. In Management and Service Science. 2009 International Conference on Management and Service Science. doi: https://doi.org/10.1109/ICMSS.2009.5302451
CR  - Manopiniwes, W. & Irohara, T. (2017). Stochastic optimisation model for integrated decisions on relief supply chains: preparedness for disaster response. International Journal of Production Research, 55 (4), 979-996. doi: https://doi.org/10.1080/00207543.2016.1211340
CR  - Massaguer, D., Balasubramanian, V.,  Mehrotra, S. & Venkatasubramanian, N. (2006). Multi-Agent Simulation of Disaster Response. Erişim adresi: https://www.researchgate.net/publication/241438415
CR  - Mohamadi, A., Yaghoubi, S. & Pishvaee, M. S. (2019). Fuzzy multi-objective stochastic programming model for disaster relief logistics considering telecommunication infrastructures: A case study. Operational Research Int Journal 19, 59–99. doi: https://doi.org/10.1007/s12351-016-0285-2
CR  - Monzón, J., Liberatore, F. & Vitoriano, B. (2020). A mathematical pre-disaster model with uncertainty and multiple criteria for facility location and network fortification. Mathematics, 8(4), 529. doi: https://doi.org/10.3390/math8040529
CR  - Öksüz, M. K. & Satoğlu Ş.İ. (2020). A two-stage stochastic model for location planning of temporary medical centers for disaster response. International Journal of Disaster Risk Reduction, 44.  doi: https://doi.org/10.1016/j.ijdrr.2019.101426
CR  - Özdamar, L., Ekinci, E. & Küçükyazici, B. (2004). Emergency logistics planning in natural disasters.  Annals of Operations Research,  129(1-4), 217-245. doi: https://doi.org/10.1023/B:ANOR.0000030690.27939.39
CR  - Safaei, A. S., Farsad, S. & Paydar, M. M. (2018). Emergency logistics planning under supply risk and demand uncertainty. Operational Research Int Journal, 1-24.
CR  - Salman, F. S. & Yücel, E. (2015). Emergency facility location under random network damage: Insights from the Istanbul case. Computers & Operations Research, 62, 266-281. doi: https://doi.org/10.1016/j.cor.2014.07.015
CR  - Torabi, S.A., Shokr, I., Tofighi, S. & Heydari, J. (2018). Integrated relief pre-positioning and procurement planning in humanitarian supply chains. Transportation Research Part E, 113, 123–146. doi: https://doi.org/10.1016/j.tre.2018.03.012
CR  - Verma, A. & Gaukler, G. M. (2011). A stochastic optimization model for positioning disaster response facilities for large scale emergencies. International Conference on Network Optimization, 547-552.
CR  - Vitoriano, B., Ortuño, M. T., Tirado, G. & Montero, J. (2011). A multi-criteria optimization model for humanitarian aid distribution. Journal of Global Optimization, 51, 189-208. doi: https://doi.org/10.1007/s10898-010-9603-z
CR  - Wang, Y., Dong, Z. S. & Hu, S. (2021). A stochastic prepositioning model for distribution of disaster supplies considering lateral transshipment. Socio-Economic Planning Sciences, 74. doi: https://doi.org/10.1016/j.seps.2020.100930
CR  - Yenice, Z.D. & Samanlıoğlu, F. (2020). A multi-objective stochastic model for an earthquake  relief network. Journal of Advanced Transportation. doi: https://doi.org/10.1155/2020/1910632
CR  - Zhan, S., Liu, S., Ignatius, J., Chen, D. & Chan, F. T. S. (2021). Disaster relief logistics under demand-supply incongruence environment: A sequential approach. Applied Mathematical Modelling 89(1), 592-609. doi: https://doi.org/10.1016/j.apm.2020.07.002
UR  - https://dergipark.org.tr/en/pub/jtom/article/913244
L1  - https://dergipark.org.tr/en/download/article-file/1697977
ER  -