Araştırma Makalesi
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A Three-Stage Mixed Integer Model Proposal in Disaster Logistics

Yıl 2021, Cilt: 5 Sayı: 1, 641 - 661, 30.06.2021

Öz

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.

Kaynakça

  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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.
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Massaguer, D., Balasubramanian, V., Mehrotra, S. & Venkatasubramanian, N. (2006). Multi-Agent Simulation of Disaster Response. Erişim adresi: https://www.researchgate.net/publication/241438415
  • 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
  • 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
  • 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
  • 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
  • Safaei, A. S., Farsad, S. & Paydar, M. M. (2018). Emergency logistics planning under supply risk and demand uncertainty. Operational Research Int Journal, 1-24.
  • 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
  • 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
  • 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.
  • 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
  • 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
  • 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
  • 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

Afet Lojistiğinde Üç Aşamalı Karma Tamsayılı Bir Model Önerisi

Yıl 2021, Cilt: 5 Sayı: 1, 641 - 661, 30.06.2021

Öz

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.

Kaynakça

  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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.
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Massaguer, D., Balasubramanian, V., Mehrotra, S. & Venkatasubramanian, N. (2006). Multi-Agent Simulation of Disaster Response. Erişim adresi: https://www.researchgate.net/publication/241438415
  • 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
  • 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
  • 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
  • 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
  • Safaei, A. S., Farsad, S. & Paydar, M. M. (2018). Emergency logistics planning under supply risk and demand uncertainty. Operational Research Int Journal, 1-24.
  • 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
  • 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
  • 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.
  • 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
  • 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
  • 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
  • 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
Toplam 35 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Endüstri Mühendisliği
Bölüm Araştırma Makalesi
Yazarlar

Hüseyin Soyöz 0000-0002-4885-6217

Bahar Özyörük Bu kişi benim 0000-0002-4885-6217

Yayımlanma Tarihi 30 Haziran 2021
Gönderilme Tarihi 11 Nisan 2021
Kabul Tarihi 5 Mayıs 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 5 Sayı: 1

Kaynak Göster

APA Soyöz, H., & Özyörük, B. (2021). Afet Lojistiğinde Üç Aşamalı Karma Tamsayılı Bir Model Önerisi. Journal of Turkish Operations Management, 5(1), 641-661.
AMA Soyöz H, Özyörük B. Afet Lojistiğinde Üç Aşamalı Karma Tamsayılı Bir Model Önerisi. JTOM. Haziran 2021;5(1):641-661.
Chicago Soyöz, Hüseyin, ve Bahar Özyörük. “Afet Lojistiğinde Üç Aşamalı Karma Tamsayılı Bir Model Önerisi”. Journal of Turkish Operations Management 5, sy. 1 (Haziran 2021): 641-61.
EndNote Soyöz H, Özyörük B (01 Haziran 2021) Afet Lojistiğinde Üç Aşamalı Karma Tamsayılı Bir Model Önerisi. Journal of Turkish Operations Management 5 1 641–661.
IEEE H. Soyöz ve B. Özyörük, “Afet Lojistiğinde Üç Aşamalı Karma Tamsayılı Bir Model Önerisi”, JTOM, c. 5, sy. 1, ss. 641–661, 2021.
ISNAD Soyöz, Hüseyin - Özyörük, Bahar. “Afet Lojistiğinde Üç Aşamalı Karma Tamsayılı Bir Model Önerisi”. Journal of Turkish Operations Management 5/1 (Haziran 2021), 641-661.
JAMA Soyöz H, Özyörük B. Afet Lojistiğinde Üç Aşamalı Karma Tamsayılı Bir Model Önerisi. JTOM. 2021;5:641–661.
MLA Soyöz, Hüseyin ve Bahar Özyörük. “Afet Lojistiğinde Üç Aşamalı Karma Tamsayılı Bir Model Önerisi”. Journal of Turkish Operations Management, c. 5, sy. 1, 2021, ss. 641-6.
Vancouver Soyöz H, Özyörük B. Afet Lojistiğinde Üç Aşamalı Karma Tamsayılı Bir Model Önerisi. JTOM. 2021;5(1):641-6.

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