Araştırma Makalesi
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Yardım malzemesi dağıtım operasyonlarının simülasyon ile analizi

Yıl 2019, Cilt: 34 Sayı: 4, 2079 - 2096, 25.06.2019

Aslı Sebatlı , Fatih Çavdur

https://doi.org/10.17341/gazimmfd.423091
Cited By: 3

Öz

Bu çalışmada, olası bir deprem sonrası kısa vadede gerçekleştirilen, yardım malzemesi dağıtım operasyonlarında merkezi ve yerel kaynakların eş-zamanlı kullanımının analiz edilmesi amacıyla bir simülasyon modeli geliştirilmiştir. Geliştirilen simülasyon modelinin senaryolarını üretmek amacıyla Amerika Birleşik Devletleri Jeoloji Araştırmaları Kurumu’nun veri tabanında yer alan önemli depremlere ilişkin bilgiler dikkate alınmıştır. Depremin büyüklüğü, derinliği ve afetzedelerin merkez üssüne olan uzaklıklarına bağlı olarak bir yapay sinir ağı aracılığıyla deprem şiddeti tahmin edilmiştir. Söz konusu deprem şiddeti ile ilişkili olarak etkilenen nüfus oranı ile afet seviyesi belirlenmektedir. Bu iki parametreye ek olarak, önceden konumlandırılan Geçici-Afet-Müdahale tesisi sayıları da bir diğer senaryo parametresi olarak dikkate alınmıştır. Simülasyon modeli ise kendi içerisinde global ve lokal olmak üzere iki alt bileşenden oluşmakta ve global bileşende merkezi kaynakların afet bölgesine gelişleri modellenirken, lokal bileşende ise afet bölgesinde yürütülen yardım malzemesi dağıtım operasyonları ele alınmaktadır. Simülasyon modeli kullanılarak, merkezi kuruluşların yardım malzemesi dağıtım faaliyetleri ile eş-zamanlı olarak, bu kuruluşlar afet bölgesine ulaşıncaya kadar geçen süreçte Geçici-Afet-Müdahale tesislerindeki envanter seviyelerinin kontrolü gerçekleştirilmektedir. Önerilen simülasyon modeli oluşturulan senaryolar altında çalıştırılmış ve elde edilen sonuçlar çeşitli performans parametreleri açısından analiz edilmiştir.


Anahtar Kelimeler

Afet operasyonları yönetimi insani lojistik yardım malzemesi dağıtımı yapay sinir ağları simülasyon

Kaynakça

  • Altay, N., Green, W.G., OR/MS research in disaster operations management, European Journal of Operational Research, 175, 475-493, 2006.
  • Natarajarathinam, M., Capar, I., Narayanan, A., Managing supply chains in times of crisis: a review of literature and insights, International Journal of Physical Distribution & Logistics Management, 39 (7), 535-573, 2009.
  • Galindo, G., Batta, R., Review of recent developments in OR/MS research in disaster operations management, European Journal of Operational Research, 230, 201-211, 2013.
  • Sheu, J. B., Challenges of emergency logistics management, Transportation Research Part E: Logistics and Transportation Review, 43 (6), 655-659, 2007.
  • Caunhye, A. M., Nie, X., Pokharel, S., Optimization models in emergency logistics: A literature review, Socio-economic planning sciences, 46 (1), 4-13, 2012.
  • Xu, X., Qi, Y. , Hua, Z. Forecasting demand of commodities after natural disasters, Expert Systems with Applications, 37 (6), 4313-4317, 2010.
  • Holguin-Veras, J., Taniguchi, E., Jaller, M., Aros-Vera, F., Ferreira, F., Thompson, R. G., The Tohoku disasters: Chief lessons concerning the post disaster humanitarian logistics response and policy implications, Transportation research part A: policy and practice, 69, 86-104, 2014.
  • Fikar, C., Gronalt, M., Hirsch, P., A decision support system for coordinated disaster relief distribution, Expert Systems with Applications, 57, 104-116, 2016.
  • Cavdur, F., Kose-Kucuk, M., Sebatli, A., Allocation of temporary disaster response facilities under demand uncertainty: An earthquake case study, International Journal of Disaster Risk Reduction, 19, 159-166, 2016.
  • Jain, S., McLean, C., Simulation for emergency response: a framework for modeling and simulation for emergency response, 35th Winter Simulation Conference, New Orleans-LA-ABD, 1068-1076, 07-10 Aralık, 2003.
  • Bankes, S., Exploratory modeling for policy analysis, Operations Research 41 (3), 435–449, 1993.
  • Özdamar, L., Ertem, M. A., Models, solutions and enabling technologies in humanitarian logistics, European Journal of Operational Research, 244 (1), 55-65, 2015.
  • Steward, D., Wan, T. T., The role of simulation and modeling in disaster management, Journal of medical systems, 31 (2), 125-130, 2007.
  • Wagner, N., Agrawal, V., An agent-based simulation system for concert venue crowd evacuation modeling in the presence of a fire disaster, Expert Systems with Applications, 41 (6), 2807-2815, 2014.
  • Ha, V., Lykotrafitis, G., Agent-based modeling of a multi-room multi-floor building emergency evacuation, Physica A: Statistical Mechanics and its Applications, 391 (8), 2740-2751, 2012.
  • Pidd, M., De Silva, F. N., Eglese, R. W., A simulation model for emergency evacuation, European Journal of operational research, 90 (3), 413-419, 1996.
  • De Silva, F. N., Eglese, R. W., Integrating simulation modelling and GIS: spatial decision support systems for evacuation planning, Journal of the Operational Research Society, 423-430, 2000.
  • Southworth, F., Regional evacuation modeling: a state-of-the-art review, 1991.
  • Chen, X., Meaker, J. W., Zhan, F. B., Agent-based modeling and analysis of hurricane evacuation procedures for the Florida Keys, Natural Hazards, 38 (3), 321, 2006.
  • Chen, X., Zhan, F. B., Agent-based modelling and simulation of urban evacuation: relative effectiveness of simultaneous and staged evacuation strategies, Journal of the Operational Research Society, 59 (1), 25-33, 2008.
  • Kimms, A., Maassen, K. C., Optimization and simulation of traffic flows in the case of evacuating urban areas, OR spectrum, 33 (3), 571-593, 2011.
  • Zou, N., Yeh, S. T., Chang, G. L., Marquess, A., Zezeski, M. Simulation-based emergency evacuation system for Ocean City, Maryland, during hurricanes, Transportation Research Record: Journal of the Transportation Research Board, (1922), 138-148, 2005.
  • Albores, P., Shaw, D., Responding to terrorist attacks and natural disasters: a case study using simulation, 37th Winter Simulation Conference, Orlando-Florida-ABD, 886-894, 4-7 Aralık, 2005.
  • Albores, P., Shaw, D., Government preparedness: Using simulation to prepare for a terrorist attack, Computers & Operations Research, 35 (6), 1924-1943, 2008.
  • Christie, P. Maria Joseph, and Reuven R. Levary., The use of simulation in planning the transportation of patients to hospitals following a disaster, Journal of medical systems, 22 (5), 289-300, 1998.
  • Chiu, Y. C., Mirchandani, P. B., Online behavior-robust feedback information routing strategy for mass evacuation, IEEE Transactions on Intelligent Transportation Systems, 9 (2), 264-274, 2008.
  • Sheffi, Y., Mahmassani, H., Powell, W. B., A transportation network evacuation model. Transportation research part A: general, 16 (3), 209-218, 1982.
  • Tovia, F., An emergency logistics response system for natural disasters, International Journal of Logistics Research and Applications, 10 (3), 173-186, 2007.
  • Afshar, A., Rasekh, A., Afshar M. H., Risk-based optimization of large flood-diversion systems using genetic algorithms, Engineering Optimization, 41 (3), 259-273, 2009.
  • Mahecha, R. S. M., Akhavan-Tabatabaei, R., A location model for storage of emergency supplies to respond to technological accidents in Bogota, Winter Simulation Conference, Berlin-Almanya, 2413-2424, 9-12 Aralık, 2012.
  • Banomyong, R., Sopadang, A., Using Monte Carlo simulation to refine emergency logistics response models: a case study, International Journal of Physical Distribution & Logistics Management, 40 (8/9), 709-721, 2010.
  • Hawe, G. I., Coates, G., Wilson, D. T., Crouch, R. S., Agent-based simulation of emergency response to plan the allocation of resources for a hypothetical two-site major incident, Engineering Applications of Artificial Intelligence, 46, 336-345, 2015.
  • Norena, D., Akhavan-Tabatabaei, R., Yamín, L., Ospina, W., Using discrete event simulation to evaluate the logistics of medical attention during the relief operations in an earthquake in Bogota, Winter Simulation Conference, Phoenix-AZ-ABD, 2666-2678, 11-14 Aralık, 2011.
  • Kılıç, A., Gökçe, M. A., Dinçer, M. C., Integrated Modeling of Disaster Emergency Response Activities Using Simulation: Bornova Case Study, Anadolu University Journal of Science and Technology–A Applied Sciences and Engineering, 17 (2), 337-356, 2016.
  • Parwanto, N. B., Morohosi, H., Oyama, T., Applying Network Flow Optimization Techniques to Improve Relief Goods Transport Strategies under Emergency Situation, American Journal of Operations Research, 5 (03), 95, 2015.
  • Lee, Y. M., Ghosh, S., Ettl, M., Simulating distribution of emergency relief supplies for disaster response operations, Winter Simulation Conference, Austin-TX-ABD, 2797-2808, 13-16 Aralık, 2009.
  • Wu, D. D., Liu, J., Olson, D. L., Simulation Decision System on the Preparation of Emergency Resources Using System Dynamics, Systems Research and Behavioral Science, 32 (6), 603-615, 2015.
  • Afet ve Acil Durum Yönetimi Başkanlığı. Türkiye Afet Müdahale Planı. Yayın tarihi Aralık, 2013.
  • Amerika Birleşik Devletleri Jeoloji Araştırmaları Kurumu. Önemli Depremler Arşivi. https://earthquake.usgs.gov/earthquakes/browse/significant.php. Erişim tarihi Nisan 11, 2017.
  • Richter, C. F., An instrumental earthquake magnitude scale, Bulletin of the Seismological Society of America, 25 (1), 1-32, 1935.
  • Amerika Birleşik Devletleri Jeoloji Araştırmaları Kurumu. The Severity of an Earthquake, USGS General Interest Publication 1989-288-913. https://earthquake.usgs.gov/learn/topics/mercalli.php. Erişim tarihi Nisan 11, 2017.
  • Wood, H. O., Neumann, F., Modified Mercalli intensity scale of 1931, Bulletin of the Seismological Society of America, 21 (4), 277-283, 1931.

Yıl 2019, Cilt: 34 Sayı: 4, 2079 - 2096, 25.06.2019

Aslı Sebatlı , Fatih Çavdur

https://doi.org/10.17341/gazimmfd.423091
Cited By: 3

Öz

In this study, we develop a simulation model to analyze the simultaneous usage of both local and global resources in relief supplies distribution operations. In order to generate the scenarios of the simulation model, we use the significant earthquakes archive of the United States Geological Survey. We estimate earthquake intensity using the magnitude, depth and distance to the epicenter of an earthquake via an artificial neural network. In relation to estimated earthquake intensity, we determine the affected population rate and the disaster level. In addition to these two parameters, the number of pre-positioned Temporary-Disaster-Response facilities is presented as another scenario parameter. Our simulation model includes two main components as global and local, where we model the arrivals of the resources of central humanitarian organizations and local relief supplies distribution operations in the global and local components, respectively. Using the simulation model, inventory levels of Temporary-Disaster-Response facilities are controlled simultaneously with the relief supplies distribution operations of central humanitarian organizations. Proposed simulation model is run with the scenarios generated and the results are analyzed in terms of some performance measures.


Anahtar Kelimeler

Disaster operations management humanitarian logistics relief supplies distribution artificial neural networks simulation

Kaynakça

  • Altay, N., Green, W.G., OR/MS research in disaster operations management, European Journal of Operational Research, 175, 475-493, 2006.
  • Natarajarathinam, M., Capar, I., Narayanan, A., Managing supply chains in times of crisis: a review of literature and insights, International Journal of Physical Distribution & Logistics Management, 39 (7), 535-573, 2009.
  • Galindo, G., Batta, R., Review of recent developments in OR/MS research in disaster operations management, European Journal of Operational Research, 230, 201-211, 2013.
  • Sheu, J. B., Challenges of emergency logistics management, Transportation Research Part E: Logistics and Transportation Review, 43 (6), 655-659, 2007.
  • Caunhye, A. M., Nie, X., Pokharel, S., Optimization models in emergency logistics: A literature review, Socio-economic planning sciences, 46 (1), 4-13, 2012.
  • Xu, X., Qi, Y. , Hua, Z. Forecasting demand of commodities after natural disasters, Expert Systems with Applications, 37 (6), 4313-4317, 2010.
  • Holguin-Veras, J., Taniguchi, E., Jaller, M., Aros-Vera, F., Ferreira, F., Thompson, R. G., The Tohoku disasters: Chief lessons concerning the post disaster humanitarian logistics response and policy implications, Transportation research part A: policy and practice, 69, 86-104, 2014.
  • Fikar, C., Gronalt, M., Hirsch, P., A decision support system for coordinated disaster relief distribution, Expert Systems with Applications, 57, 104-116, 2016.
  • Cavdur, F., Kose-Kucuk, M., Sebatli, A., Allocation of temporary disaster response facilities under demand uncertainty: An earthquake case study, International Journal of Disaster Risk Reduction, 19, 159-166, 2016.
  • Jain, S., McLean, C., Simulation for emergency response: a framework for modeling and simulation for emergency response, 35th Winter Simulation Conference, New Orleans-LA-ABD, 1068-1076, 07-10 Aralık, 2003.
  • Bankes, S., Exploratory modeling for policy analysis, Operations Research 41 (3), 435–449, 1993.
  • Özdamar, L., Ertem, M. A., Models, solutions and enabling technologies in humanitarian logistics, European Journal of Operational Research, 244 (1), 55-65, 2015.
  • Steward, D., Wan, T. T., The role of simulation and modeling in disaster management, Journal of medical systems, 31 (2), 125-130, 2007.
  • Wagner, N., Agrawal, V., An agent-based simulation system for concert venue crowd evacuation modeling in the presence of a fire disaster, Expert Systems with Applications, 41 (6), 2807-2815, 2014.
  • Ha, V., Lykotrafitis, G., Agent-based modeling of a multi-room multi-floor building emergency evacuation, Physica A: Statistical Mechanics and its Applications, 391 (8), 2740-2751, 2012.
  • Pidd, M., De Silva, F. N., Eglese, R. W., A simulation model for emergency evacuation, European Journal of operational research, 90 (3), 413-419, 1996.
  • De Silva, F. N., Eglese, R. W., Integrating simulation modelling and GIS: spatial decision support systems for evacuation planning, Journal of the Operational Research Society, 423-430, 2000.
  • Southworth, F., Regional evacuation modeling: a state-of-the-art review, 1991.
  • Chen, X., Meaker, J. W., Zhan, F. B., Agent-based modeling and analysis of hurricane evacuation procedures for the Florida Keys, Natural Hazards, 38 (3), 321, 2006.
  • Chen, X., Zhan, F. B., Agent-based modelling and simulation of urban evacuation: relative effectiveness of simultaneous and staged evacuation strategies, Journal of the Operational Research Society, 59 (1), 25-33, 2008.
  • Kimms, A., Maassen, K. C., Optimization and simulation of traffic flows in the case of evacuating urban areas, OR spectrum, 33 (3), 571-593, 2011.
  • Zou, N., Yeh, S. T., Chang, G. L., Marquess, A., Zezeski, M. Simulation-based emergency evacuation system for Ocean City, Maryland, during hurricanes, Transportation Research Record: Journal of the Transportation Research Board, (1922), 138-148, 2005.
  • Albores, P., Shaw, D., Responding to terrorist attacks and natural disasters: a case study using simulation, 37th Winter Simulation Conference, Orlando-Florida-ABD, 886-894, 4-7 Aralık, 2005.
  • Albores, P., Shaw, D., Government preparedness: Using simulation to prepare for a terrorist attack, Computers & Operations Research, 35 (6), 1924-1943, 2008.
  • Christie, P. Maria Joseph, and Reuven R. Levary., The use of simulation in planning the transportation of patients to hospitals following a disaster, Journal of medical systems, 22 (5), 289-300, 1998.
  • Chiu, Y. C., Mirchandani, P. B., Online behavior-robust feedback information routing strategy for mass evacuation, IEEE Transactions on Intelligent Transportation Systems, 9 (2), 264-274, 2008.
  • Sheffi, Y., Mahmassani, H., Powell, W. B., A transportation network evacuation model. Transportation research part A: general, 16 (3), 209-218, 1982.
  • Tovia, F., An emergency logistics response system for natural disasters, International Journal of Logistics Research and Applications, 10 (3), 173-186, 2007.
  • Afshar, A., Rasekh, A., Afshar M. H., Risk-based optimization of large flood-diversion systems using genetic algorithms, Engineering Optimization, 41 (3), 259-273, 2009.
  • Mahecha, R. S. M., Akhavan-Tabatabaei, R., A location model for storage of emergency supplies to respond to technological accidents in Bogota, Winter Simulation Conference, Berlin-Almanya, 2413-2424, 9-12 Aralık, 2012.
  • Banomyong, R., Sopadang, A., Using Monte Carlo simulation to refine emergency logistics response models: a case study, International Journal of Physical Distribution & Logistics Management, 40 (8/9), 709-721, 2010.
  • Hawe, G. I., Coates, G., Wilson, D. T., Crouch, R. S., Agent-based simulation of emergency response to plan the allocation of resources for a hypothetical two-site major incident, Engineering Applications of Artificial Intelligence, 46, 336-345, 2015.
  • Norena, D., Akhavan-Tabatabaei, R., Yamín, L., Ospina, W., Using discrete event simulation to evaluate the logistics of medical attention during the relief operations in an earthquake in Bogota, Winter Simulation Conference, Phoenix-AZ-ABD, 2666-2678, 11-14 Aralık, 2011.
  • Kılıç, A., Gökçe, M. A., Dinçer, M. C., Integrated Modeling of Disaster Emergency Response Activities Using Simulation: Bornova Case Study, Anadolu University Journal of Science and Technology–A Applied Sciences and Engineering, 17 (2), 337-356, 2016.
  • Parwanto, N. B., Morohosi, H., Oyama, T., Applying Network Flow Optimization Techniques to Improve Relief Goods Transport Strategies under Emergency Situation, American Journal of Operations Research, 5 (03), 95, 2015.
  • Lee, Y. M., Ghosh, S., Ettl, M., Simulating distribution of emergency relief supplies for disaster response operations, Winter Simulation Conference, Austin-TX-ABD, 2797-2808, 13-16 Aralık, 2009.
  • Wu, D. D., Liu, J., Olson, D. L., Simulation Decision System on the Preparation of Emergency Resources Using System Dynamics, Systems Research and Behavioral Science, 32 (6), 603-615, 2015.
  • Afet ve Acil Durum Yönetimi Başkanlığı. Türkiye Afet Müdahale Planı. Yayın tarihi Aralık, 2013.
  • Amerika Birleşik Devletleri Jeoloji Araştırmaları Kurumu. Önemli Depremler Arşivi. https://earthquake.usgs.gov/earthquakes/browse/significant.php. Erişim tarihi Nisan 11, 2017.
  • Richter, C. F., An instrumental earthquake magnitude scale, Bulletin of the Seismological Society of America, 25 (1), 1-32, 1935.
  • Amerika Birleşik Devletleri Jeoloji Araştırmaları Kurumu. The Severity of an Earthquake, USGS General Interest Publication 1989-288-913. https://earthquake.usgs.gov/learn/topics/mercalli.php. Erişim tarihi Nisan 11, 2017.
  • Wood, H. O., Neumann, F., Modified Mercalli intensity scale of 1931, Bulletin of the Seismological Society of America, 21 (4), 277-283, 1931.
Toplam 42 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Aslı Sebatlı 0000-0002-9445-6740

Fatih Çavdur 0000-0001-8054-5606

Yayımlanma Tarihi 25 Haziran 2019
Gönderilme Tarihi 12 Mayıs 2018
Kabul Tarihi 12 Şubat 2019
Yayımlandığı Sayı Yıl 2019 Cilt: 34 Sayı: 4

Kaynak Göster

APA Sebatlı, A., & Çavdur, F. (2019). Yardım malzemesi dağıtım operasyonlarının simülasyon ile analizi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 34(4), 2079-2096. https://doi.org/10.17341/gazimmfd.423091
AMA Sebatlı A, Çavdur F. Yardım malzemesi dağıtım operasyonlarının simülasyon ile analizi. GUMMFD. Haziran 2019;34(4):2079-2096. doi:10.17341/gazimmfd.423091
Chicago Sebatlı, Aslı, ve Fatih Çavdur. “Yardım Malzemesi dağıtım operasyonlarının simülasyon Ile Analizi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 34, sy. 4 (Haziran 2019): 2079-96. https://doi.org/10.17341/gazimmfd.423091.
EndNote Sebatlı A, Çavdur F (01 Haziran 2019) Yardım malzemesi dağıtım operasyonlarının simülasyon ile analizi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 34 4 2079–2096.
IEEE A. Sebatlı ve F. Çavdur, “Yardım malzemesi dağıtım operasyonlarının simülasyon ile analizi”, GUMMFD, c. 34, sy. 4, ss. 2079–2096, 2019, doi: 10.17341/gazimmfd.423091.
ISNAD Sebatlı, Aslı - Çavdur, Fatih. “Yardım Malzemesi dağıtım operasyonlarının simülasyon Ile Analizi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 34/4 (Haziran 2019), 2079-2096. https://doi.org/10.17341/gazimmfd.423091.
JAMA Sebatlı A, Çavdur F. Yardım malzemesi dağıtım operasyonlarının simülasyon ile analizi. GUMMFD. 2019;34:2079–2096.
MLA Sebatlı, Aslı ve Fatih Çavdur. “Yardım Malzemesi dağıtım operasyonlarının simülasyon Ile Analizi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, c. 34, sy. 4, 2019, ss. 2079-96, doi:10.17341/gazimmfd.423091.
Vancouver Sebatlı A, Çavdur F. Yardım malzemesi dağıtım operasyonlarının simülasyon ile analizi. GUMMFD. 2019;34(4):2079-96.

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