A HUMANITARIAN RELIEF LOGISTICS IN DISASTER OPERATIONS MANAGEMENT

Sosyal ve ekonomik faktörlere bağlı olarak, afet operasyon yönetimi (AOY) konuları içerisinde insani yardım lojistiği (lYL) hem akademinin hem de uygulayıcıların ilgisini çekmektedir. Dolayısı ile, son zamanlardaki ve en yeni makalelerin kapsamlı bir literatür incelemesinin yapılması, geçmiş çalışmaların çerçevesinin çizilmesi ve gelecek çalışmalara ışık tutulması açısından can alıcıdır. Bu makalede, AOY'de lYL faaliyetleri için optimizasyon ve karar bilimi uygulamalarını kapsayan en yeni literatür incelemesi sunulmaktadır. Ocak 2007 ve Mayıs 2015 arasında basılan toplam 220 makale seçilmiş ve incelenmiştir. Özellikle, tesis yer seçimi, envanter yönetimi, yardım dağıtım planlaması, enkaz temizleme ve toparlanma operasyonları, karar destek sistemleri, ve ilgili lYL faaliyetleri ile uyumlu olan diger operasyonlar incelenmiştir. Son olarak, ileriki araştırma fırsatlarının açıklığa kavuşturulması ve önerilmesi amacıyla literatürdeki eksikliklerin ve gelecek çalışmaların bir listesi oluşturulmuştur.

AFET OPERASYON YÖNETİMİNDE INSANİ YARDIM LOJİSTİĞİ İÇİN KAPSAMLI BİR LİTERA TÜR İNCELEMESİ

Based on social and economic factors, humanitarian relief logistics (HRL) in disaster operations management (DOM) issues have attracted attention among both academia and practitioners. Hence, comprehensive literature review of recent and state of the art papers is vital to draw framework of the past, and to shed light on future directions. ln this paper, state of the art literature review is presented for optimization and decision science to HRL activities in DOM. total of 220 papers published between January 2007 and May 2015 are selected and reviewed. ln particular, the facility location, inventory management, relief distribution planning, debris cleaning and recovery operations, decision support systems, and other operations in accordance with the relevant HRL activities in DOM are investigated. Finally, list of research gap and further research are identified to clarify and to suggest future research opportunities.

PDF

___

Abounacer, R., Rekik, M., & Renaud, J. (2014). An exact solution approach for multi-objective location–transportation problem for disaster response. Computers &
Operations Research, 41, 83-93. $GıYDU % 0HUW $ ,QWHUQDWLRQDO GLVDVWHU UHOLHI SODQQLQJ ZLWK IX]] credibility. Fuzzy Optimization and Decision Making, 9(4), 413-433. Afsar, H. M., Prins, C., & Santos, A. C. (2014). Exact and heuristic algorithms for solving the generalized vehicle routing problem with flexible fleet size.
International Transactions in Operational Research, 21(1), 153-175. Afshar, A., & Haghani, A. (2012). Modeling integrated supply chain logistics in real-time large-scale disaster relief operations. Socio-Economic Planning Sciences,
Ahmadi, M., Seifi, A., & Tootooni, B. (2015). A humanitarian logistics model for disaster relief operation considering network failure and standard relief time: A case study on San Francisco district. Transportation Research Part E: Logistics and Transportation Review, 75, 145-163.
$NJQ İ *PşEXğD ) 7DQVHO % 5LVN EDVHG IDFLOLW ORFDWLRQ E using fault tree analysis in disaster management. Omega, 52, 168-179. Aksu, D. T., & Ozdamar, L. (2014). A mathematical model for post-disaster road restoration: Enabling accessibility and evacuation. Transportation Research Part E:
Logistics and Transportation Review, 61, 56-67. Allahviranloo, M., Chow, J. Y., & Recker, W. W. (2014). Selective vehicle routing problems under uncertainty without recourse. Transportation Research Part E:
Logistics and Transportation Review, 62, 68-88. Ertem, M.A., & Buyurgan, N. (2011). An auction-based framework for resource allocation in disaster relief. Journal of Humanitarian Logistics and Supply Chain Management, 1(2), 170-188.
Altay, N., & Green, W. G. (2006). OR/MS research in disaster operations management. European Journal of Operational Research, 175(1), 475-493.
Anaya-Arenas, A. M., Renaud, J., & Ruiz, A. (2014). Relief distribution networks: a systematic review. Annals of Operations Research, 223(1), 53-79.
Bagchi, A., Paul, J. A., & Maloni, M. (2011). Improving bid efficiency for humanitarian food aid procurement. International Journal of Production Economics, (1), 238-245.
Balcik, B., & Ak, D. (2014). Supplier selection for framework agreements in humanitarian relief. Production and Operations Management, 23(6), 1028-1041.
Balcik, B., & Beamon, B. M. (2008). Facility location in humanitarian relief.
International Journal of Logistics, 11(2), 101-121. Balcik, B., Beamon, B. M., & Smilowitz, K. (2008). Last mile distribution in humanitarian relief. Journal of Intelligent Transportation Systems, 12(2), 51-63.
Balcik, B., Iravani, S., & Smilowitz, K. (2014). Multi-vehicle sequential resource allocation for a nonprofit distribution system. IIE Transactions, 46(12), 1279-1297.
Barzinpour, F., & Esmaeili, V. (2014). A multi-objective relief chain location distribution model for urban disaster management. The International Journal of
Advanced Manufacturing Technology, 70(5-8), 1291-1302.
Barzinpour, F., Saffarian, M., Makoui, A., & Teimoury, E. (2014). Metaheuristic
Algorithm for Solving Biobjective Possibility Planning Model of Location- Allocation in Disaster Relief Logistics. Journal of Applied Mathematics, 2014.
Bastian, N. D., Griffin, P. M., Spero, E., & Fulton, L. V. (2015). Multi-criteria logistics modeling for military humanitarian assistance and disaster relief aerial delivery operations. Optimization Letters, 1-33.
Ben-Tal, A., Do Chung, B., Mandala, S. R., & Yao, T. (2011). Robust optimization for emergency logistics planning: Risk mitigation in humanitarian relief supply chains. Transportation Research Part B: Methodological, 45(8), 1177-1189.
Berkoune, D., Renaud, J., Rekik, M., & Ruiz, A. (2012). Transportation in disaster response operations. Socio-Economic Planning Sciences, 46(1), 23-32.
Besiou, M., Pedraza-Martinez, A. J., & Van Wassenhove, L. N. (2014). Vehicle
Supply Chains in Humanitarian Operations: Decentralization, Operational Mix, and Earmarked Funding. Production and Operations Management, 23(11), 1950-1965.
Bish, D. R. (2011). Planning for a bus-based evacuation. OR spectrum, 33(3), 629
Bozkurt, M., & Duran, S. (2012). Effects of natural disaster trends: a case study for expanding the pre-positioning network of CARE international. International Journal of Environmental Research and Public Health, 9(8), 2863-2874.
Bozorgi-Amiri, A., Jabalameli, M. S., Alinaghian, M., & Heydari, M. (2012). A modified particle swarm optimization for disaster relief logistics under uncertain environment. The International Journal of Advanced Manufacturing Technology, (1-4), 357-371.
Campbell, A. M., & Jones, P. C. (2011). Prepositioning supplies in preparation for disasters. European Journal of Operational Research, 209(2), 156-165.
Campbell, A. M., Vandenbussche, D., & Hermann, W. (2008). Routing for relief efforts. Transportation Science, 42(2), 127-145.
Caunhye, A. M., Li, M., & Nie, X. (2015). A location-allocation model for casualty response planning during catastrophic radiological incidents. Socio-Economic Planning Sciences, 50, 32-44.
Caunhye, A. M., Nie, X., & Pokharel, S. (2012). Optimization models in emergency logistics: A literature review. Socio-Economic Planning Sciences, 46(1), 4-13.
Celik, E., Gumus, A. T., & Alegoz, M. (2014). A trapezoidal type-2 fuzzy MCDM method to identify and evaluate critical success factors for humanitarian relief logistics management. Journal of Intelligent and Fuzzy Systems, 27 (6), 2847-2855.
Çelik, M., Ergun, Ö., & Keskinocak, P. (2015). The Post-Disaster Debris Clearance Problem Under Incomplete Information. Operations Research, 63(1), 65-85. Chakravarty, A. K. (2014). Humanitarian relief chain: Rapid response under uncertainty. International Journal of Production Economics, 151, 146-157.
Chang, F. S., Wu, J. S., Lee, C. N., & Shen, H. C. (2014). Greedy-search-based multi-objective genetic algorithm for emergency logistics scheduling. Expert
Systems With Applications, 41(6), 2947-2956. Chang, M. S., Tseng, Y. L., & Chen, J. W. (2007). A scenario planning approach for the flood emergency logistics preparation problem under uncertainty.
Transportation Research Part E: Logistics and Transportation Review, 43(6), 737- Charles, A., & Lauras, M. (2011). An enterprise modelling approach for better optimisation modelling: application to the humanitarian relief chain coordination problem. Or Spectrum, 33(3), 815-841.
Chen, H. K., Chou, H. W., Ho, P. S., & Wang, H. (2011). Real-Time Vehicle Routing for Repairing Damaged Infrastructures Due to Natural Disasters. Mathematical Problems in Engineering, 2011.
Chen, L. M., Liu, Y. E., & Yang, S. J. S. (2015). Robust supply chain strategies for recovering from unanticipated disasters. Transportation Research Part E: Logistics and Transportation Review, 77, 198-214.
Chi, T. H., Yang, H., & Hsiao, H. M. (2011, October). A new hierarchical facility location model and genetic algorithm for humanitarian relief. In Information Science and Service Science (NISS), 2011 5th International Conference on New Trends in (Vol. 2, pp. 367-374). IEEE.
Chou, J. S., Tsai, C. F., Chen, Z. Y., & Sun, M. H. (2014). Biological-based genetic algorithms for optimized disaster response resource allocation. Computers &
Industrial Engineering, 74, 52-67. Das, R., & Hanaoka, S. (2014). Relief inventory modelling with stochastic lead-time and demand. European Journal of Operational Research, 235(3), 616-623.
Davis, L. B., Samanlioglu, F., Qu, X., & Root, S. (2013). Inventory planning and coordination in disaster relief efforts. International Journal of Production Economics, 141(2), 561-573.
De Angelis, V., Mecoli, M., Nikoi, C., & Storchi, G. (2007). Multiperiod integrated routing and scheduling of World Food Programme cargo planes in Angola.
Computers & Operations Research, 34(6), 1601-1615. de la Torre, L.E., Dolinskaya, I. S., & Smilowitz, K. R. (2012). Disaster relief routing: Integrating research and practice. Socio-economic planning sciences, 46(1), 97.
Doerner, K. F., Gutjahr, W. J., & Nolz, P. C. (2009). Multi-criteria location planning for public facilities in tsunami-prone coastal areas. Or Spectrum, 31(3), 651-678.
Döyen, A., Aras, 1 %DUEDURVRğOX * $ WZR-echelon stochastic facility location model for humanitarian relief logistics. Optimization Letters, 6(6), 1123
Du, L., & Peeta, S. (2014). A stochastic optimization model to reduce expected post- disaster response time through pre-disaster investment decisions. Networks And Spatial Economics, 14(2), 271-295.
Du, M., & Yi, H. (2013). Research on multi-objective emergency logistics vehicle routing problem under constraint conditions. Journal of Industrial Engineering and Management, 6(1), 258-266.
Duque, P. M., & Sörensen, K. (2011). A GRASP metaheuristic to improve accessibility after a disaster. OR spectrum, 33(3), 525-542.
Duran, S., Gutierrez, M. A., & Keskinocak, P. (2011). Pre-positioning of emergency items for CARE International. Interfaces, 41(3), 223-237.
Duran, S., Gutierrez, M. A., & Keskinocak, P. (2011). Pre-positioning of emergency items for CARE International. Interfaces, 41(3), 223-237.
Ecerkale, K. (2012). A heuristic approach to Red Crescent aid depots location problem. In Fuzzy Theory and it's Applications (iFUZZY), 2012 International
Conference on (pp. 68-71). IEEE. Edrissi, A., Poorzahedy, H., Nassiri, H., & Nourinejad, M. (2013). A multi-agent optimization formulation of earthquake disaster prevention and management.
European Journal of Operational Research, 229(1), 261-275. Eftekhar, M., Masini, A., Robotis, A., & Van Wassenhove, L. N. (2014). Vehicle procurement policy for humanitarian development programs. Production and Operations Management, 23(6), 951-964.
Eiselt, H. A., & Marianov, V. (2012). Mobile phone tower location for survival after natural disasters. European journal of operational research, 216(3), 563-572.
Ergun, Ö., Gui, L., Heier Stamm, J. L., Keskinocak, P., & Swann, J. (2014).
Improving Humanitarian Operations through Technology-Enabled Collaboration. Production and Operations Management, 23(6), 1002-1014.
Ergun, O., Karakus, G., Keskinocak, P., Swann, J., & Villarreal, M. (2010). Operations research to improve disaster supply chain management. Wiley Encyclopedia of Operations Research and Management Science. http://dx.doi.org/10.1002/9780470400531.eorms0604
Ertem, M. A., & Buyurgan, N. (2013). A Procurement Auctions-Based Framework for Coordinating Platforms in Humanitarian Logistics. In Humanitarian and Relief
Logistics (pp. 111-127). Springer New York. Ertem, M. A., Buyurgan, N., & Pohl, E. A. (2012). Using announcement options in the bid construction phase for disaster relief procurement. Socio-Economic Planning Sciences, 46(4), 306-314.
Ertem, M. A., Buyurgan, N., & Rossetti, M. D. (2010). Multiple-buyer procurement auctions framework for humanitarian supply chain management. International
Journal of Physical Distribution & Logistics Management, 40(3), 202-227. Ertem, M.A, & Buyurgan, N. (2011). An auction-based framework for resource allocation in disaster relief. Journal of Humanitarian Logistics and Supply Chain
Esmaeili, V., & Barzinpour, F. (2014). Integrated decision making model for urban disaster management: A multi-objective genetic algorithm approach. International
Journal of Industrial Engineering Computations, 5(1), 55-70. Falasca, M., & Zobel, C. (2012). An optimization model for volunteer assignments in humanitarian organizations. Socio-Economic Planning Sciences, 46(4), 250-260.
Falasca, M., & Zobel, C. W. (2011). A two-stage procurement model for humanitarian relief supply chains. Journal of Humanitarian Logistics and Supply
Chain Management, 1(2), 151-169. Faturechi, R., & Miller-Hooks, E. (2014). Travel time resilience of roadway networks under disaster. Transportation research part B: methodological, 70, 47-64.
Fetter, G., & Rakes, T. (2012). Incorporating recycling into post-disaster debris disposal. Socio-Economic Planning Sciences, 46(1), 14-22.
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. Galindo, G., & Batta, R. (2013). Review of recent developments in OR/MS research in disaster operations management. European Journal of Operational Research,
Gatignon, A., Van Wassenhove, L. N., & Charles, A. (2010). The Yogyakarta earthquake: Humanitarian relief through IFRC's decentralized supply chain.
International Journal of Production Economics, 126(1), 102-110. Gonçalves, P., Leiras, A., Chawaguta, B., & Officer, L. (2013). Stochastic optimization for humanitarian aid supply and distribution of World Food
Programme (WFP) in Ethiopia. International Production and Operations Management (POM) Conference, Denver, USA. Gong, Q., & Batta, R. (2007). Allocation and reallocation of ambulances to casualty clusters in a disaster relief operation. IIE Transactions, 39(1), 27-39.
Görmez, N., Köksalan, M., & Salman, F. S. (2011). Locating disaster response facilities in Istanbul. Journal of the Operational Research Society, 62(7), 1239
Govindan, K., Soleimani, H., & Kannan, D. (2015). Reverse logistics and closed- loop supply chain: A comprehensive review to explore the future. European Journal of Operational Research, 240(3), 603-626.
Gralla, E., Goentzel, J., & Fine, C. (2014). Assessing Trade-offs among Multiple
Objectives for Humanitarian Aid Delivery Using Expert Preferences. Production and Operations Management, 23(6), 978-989. Gul, M., & Guneri, A. F. (2015). A comprehensive review of emergency department simulation applications for normal and disaster conditions. Computers & Industrial Engineering, 83, 327-344.
Gunnec, D., & Salman, F. (2007). A two-stage multi-criteria stochastic programming model for location of emergency response and distribution centers. Euro-online. org, 182-201.
Gunnec, D., & Salman, F. (2007). A two-stage multi-criteria stochastic programming model for location of emergency response and distribution centers. Euro-online. org, 182-201.
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.
Hadiguna, R. A., Kamil, I., Delati, A., & Reed, R. (2014). Implementing a web- based decision support system for disaster logistics: A case study of an evacuation location assessment for Indonesia. International Journal of Disaster Risk Reduction, , 38-47.
Han, Y., Guan, X., & Shi, L. (2011). Optimization based method for supply location selection and routing in large-scale emergency material delivery. Automation
Science and Engineering, IEEE Transactions on, 8(4), 683-693. Holguín-Veras, J., Jaller, M., Van Wassenhove, L. N., Pérez, N., & Wachtendorf, T. (2012). On the unique features of post-disaster humanitarian logistics. Journal of
Operations Management, 30(7), 494-506. Holguín-Veras, J., Pérez, N., Jaller, M., Van Wassenhove, L. N., & Aros-Vera, F. (2013). On the appropriate objective function for post-disaster humanitarian logistics models. Journal of Operations Management, 31(5), 262-280.
Hong, X., Lejeune, M. A., & Noyan, N. (2014). Stochastic network design for disaster preparedness. IIE Transactions, 47(4), 329-357.
Horner, M. W., & Downs, J. A. (2010). Optimizing hurricane disaster relief goods distribution: model development and application with respect to planning strategies. Disasters, 34(3), 821-844.
Hoyos, M. C., Morales, R. S., & Akhavan-Tabatabaei, R. (2014). OR models with stochastic components in disaster operations management: A literature survey.
Computers & Industrial Engineering, 82, 183–197. Hu, Z. H., & Sheu, J. B. (2013). Post-disaster debris reverse logistics management under psychological cost minimization. Transportation Research Part B: Methodological, 55, 118-141.
Hu, Z. H., Sheu, J. B., & Xiao, L. (2014). Post-disaster evacuation and temporary resettlement considering panic and panic spread. Transportation research part B: methodological, 69, 112-132.
Huang, K., Jiang, Y., Yuan, Y., & Zhao, L. (2015). Modeling multiple humanitarian objectives in emergency response to large-scale disasters. Transportation Research
Part E: Logistics and Transportation Review, 75, 1-17. Huang, M., Smilowitz, K. R., & Balcik, B. (2013). A continuous approximation approach for assessment routing in disaster relief. Transportation Research Part B: Methodological, 50, 20-41.
Huang, M., Smilowitz, K., & Balcik, B. (2012). Models for relief routing: Equity, efficiency and efficacy. Transportation research part E: logistics and transportation review, 48(1), 2-18.
Huang, R., Kim, S., & Menezes, M. B. (2010). Facility location for large-scale emergencies. Annals of Operations Research, 181(1), 271-286.
Iakovou, E., Ip, C. M., Douligeris, C., & Korde, A. (1997). Optimal location and capacity of emergency cleanup equipment for oil spill response. European Journal of Operational Research, 96(1), 72-80.
Irohara, T., Kuo, Y. H., & Leung, J. M. (2013). From preparedness to recovery: a tri-level programming model for disaster relief planning. In Computational Logistics
(pp. 213-228). Springer Berlin Heidelberg. Jabbarzadeh, A., Fahimnia, B., & Seuring, S. (2014). Dynamic supply chain network design for the supply of blood in disasters: A robust model with real world application. Transportation Research Part E: Logistics and Transportation Review, , 225-244.
Jeong, K. Y., Hong, J. D., & Xie, Y. (2014). Design of emergency logistics networks, taking efficiency, risk and robustness into consideration. International
Journal of Logistics Research and Applications, 17(1), 1-22. Jia, H., Ordóñez, F., & Dessouky, M. (2007). A modeling framework for facility location of medical services for large-scale emergencies. IIE transactions, 39(1), 41
Jia, H., Ordóñez, F., & Dessouky, M. M. (2007). Solution approaches for facility location of medical supplies for large-scale emergencies. Computers & Industrial Engineering, 52(2), 257-276.
Jin, S., Jeong, S., Kim, J., & Kim, K. (2014). A logistics model for the transport of disaster victims with various injuries and survival probabilities. Annals of
Operations Research, 1-17. Jotshi, A., Gong, Q., & Batta, R. (2009). Dispatching and routing of emergency vehicles in disaster mitigation using data fusion. Socio-Economic Planning Sciences, (1), 1-24.
Kelle, P., Schneider, H., & Yi, H. (2014). Decision alternatives between expected cost minimization and worst case scenario in emergency supply–Second revision.
International Journal of Production Economics, 157, 250-260. ıOFı ) .DUD % < %R]NDD % /RFDWLQJ WHPSRUDU VKHOWHU DUHDV DIWHU an earthquake: A case for Turkey. European Journal of Operational Research, Klibi, W., Ichoua, S., & Martel, A. (2013). Prepositioning emergency supplies to support disaster relief: a stochastic programming approach. Faculté des sciences de l'administration, Université Laval.
Kou, G., Ergu, D., & Shi, Y. (2014). An integrated expert system for fast disaster assessment. Computers & Operations Research, 42, 95-107.
Kovács, G., & Spens, K. M. (2007). Humanitarian logistics in disaster relief operations. International Journal of Physical Distribution & Logistics Management, (2), 99-114.
Kunz, N., Reiner, G., & Gold, S. (2014). Investing in disaster management capabilities versus pre-positioning inventory: A new approach to disaster preparedness. International Journal of Production Economics, 157, 261-272.
Lassiter, K., Khademi, A., & Taaffe, K. M. (2015). A robust optimization approach to volunteer management in humanitarian crises. International Journal of
Production Economics, 163, 97-111. Leiras, A., de Brito Jr, I., Queiroz Peres, E., Rejane Bertazzo, T., & Tsugunobu Yoshida Yoshizaki, H. (2014). Literature review of humanitarian logistics research: trends and challenges. Journal of Humanitarian Logistics and Supply Chain
Li, A. C., Nozick, L., Xu, N., & Davidson, R. (2012). Shelter location and transportation planning under hurricane conditions. Transportation Research Part
E: Logistics and Transportation Review, 48(4), 715-729. Li, L., Jin, M., & Zhang, L. (2011). Sheltering network planning and management with a case in the Gulf Coast region. International Journal of Production Economics, 131(2), 431-440.
Liang, L., Wang, X., & Gao, J. (2012). An option contract pricing model of relief material supply chain. Omega, 40(5), 594-600.
Liberatore, F., Ortuño, M. T., Tirado, G., Vitoriano, B., & Scaparra, M. P. (2014). A hierarchical compromise model for the joint optimization of recovery operations and distribution of emergency goods in Humanitarian Logistics. Computers &
Operations Research, 42, 3-13. Liberatore, F., Pizarro, C., Blas, C., Ortuo, M., & Vitoriano, B. (2013). Uncertainty in humanitarian logistics for disaster management. A review. In Decision aid models for disaster management and emergencies. In B. Vitoriano, J. Montero, & D. Ruan
(Eds.). Atlantis computational intelligence systems (Vol. 7, pp. 45–74). Atlantis Press. Lin, Y. H., Batta, R., Rogerson, P. A., Blatt, A., & Flanigan, M. (2012). Location of temporary depots to facilitate relief operations after an earthquake. Socio-Economic
Planning Sciences, 46(2), 112-123. Lin, Y. H., Batta, R., Rogerson, P. A., Blatt, A., & Flanigan, M. (2011). A logistics model for emergency supply of critical items in the aftermath of a disaster. Socio
Economic Planning Sciences, 45(4), 132-145. Liu, C., Chen, Z. H., & Gong, Y. Y. (2013). Site selection of emergency material warehouse under fuzzy environment. Journal of Central South University, 20, 1610
Liu, Y., & Guo, B. (2014). A Lexicographic Approach to Postdisaster Relief Logistics Planning Considering Fill Rates and Costs under Uncertainty. Mathematical Problems in Engineering, 2014.
Lodree, E. J., & Taskin, S. (2009). Supply chain planning for hurricane response with wind speed information updates. Computers & Operations Research, 36(1), 2
Lodree, E. J., Ballard, K. N., & Song, C. H. (2012). Pre-positioning hurricane supplies in a commercial supply chain. Socio-Economic Planning Sciences, 46(4), 305.
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. Lu, C. C., & Sheu, J. B. (2013). Robust vertex p-center model for locating urgent relief distribution centers. Computers & Operations Research, 40(8), 2128-2137.
Luis, E., Dolinskaya, I. S., & Smilowitz, K. R. (2012). Disaster relief routing: Integrating research and practice. Socio-economic planning sciences, 46(1), 88-97. McCoy, J. H., & Brandeau, M. L. (2011). Efficient stockpiling and shipping policies for humanitarian relief: UNHCR’s inventory challenge. OR spectrum, 33(3), 673
Mete, H. O., & Zabinsky, Z. B. (2007). Preparing for disasters: medical supply location and distribution. In Proceedings of the INFORMS conference, Seattle, WA.
Mete, H. O., & Zabinsky, Z. B. (2010). Stochastic optimization of medical supply location and distribution in disaster management. International Journal of
Production Economics, 126(1), 76-84. Mirzapour, S. A., Wong, K. Y., & Govindan, K. (2013). A Capacitated Location Allocation Model for Flood Disaster Service Operations with Border Crossing Passages and Probabilistic Demand Locations. Mathematical Problems in Engineering, 2013.
Mohan, S., Gopalakrishnan, M., & Mizzi, P. J. (2013). Improving the efficiency of a non-profit supply chain for the food insecure. International Journal of Production Economics, 143(2), 248-255.
Murali, P., Ordóñez, F., & Dessouky, M. M. (2012). Facility location under demand uncertainty: Response to a large-scale bio-terror attack. Socio-Economic Planning Sciences, 46(1), 78-87.
Na, H. S., & Banerjee, A. (2015). A disaster evacuation network model for transporting multiple priority evacuees. IIE Transactions, http://dx.doi.org/10.1080/0740817X.2015.1040929.
Nahleh, Y. L., Kumar, A., & Daver, F. (2013). Predicting Relief Materials' Demand for EmergencyLogistics Planning using ARENA Input Analyzer, International
Journal of Engineering Science and Innovative Technology, 2(5), 318-327. Najafi, M., Eshghi, K., & de Leeuw, S. (2014). A dynamic dispatching and routing model to plan/re-plan logistics activities in response to an earthquake. OR spectrum, (2), 323-356.
Najafi, M., Eshghi, K., & Dullaert, W. (2013). A multi-objective robust optimization model for logistics planning in the earthquake response phase. Transportation
Research Part E: Logistics and Transportation Review, 49(1), 217-249. Naji-Azimi, Z., Renaud, J., Ruiz, A., & Salari, M. (2012). A covering tour approach to the location of satellite distribution centers to supply humanitarian aid. European
Journal of Operational Research, 222(3), 596-605. Nappi, M. M. L., & Souza, J. C. (2015). Disaster management: hierarchical structuring criteria for selection and location of temporary shelters. Natural Hazards,
Natarajarathinam, M., Capar, I., & Narayanan, A. (2009). Managing supply chains in times of crisis: a review of literature and insights. International Journal of
Physical Distribution & Logistics Management, 39(7), 535-573. Nolz, P. C., Doerner, K. F., Gutjahr, W. J., & Hartl, R. F. (2010). A bi-objective metaheuristic for disaster relief operation planning. In Advances in multi-objective nature inspired computing (pp. 167-187). Springer Berlin Heidelberg.
Nolz, P. C., Semet, F., & Doerner, K. F. (2011). Risk approaches for delivering disaster relief supplies. OR spectrum, 33(3), 543-569.
Noyan, N. (2012). Risk-averse two-stage stochastic programming with an application to disaster management. Computers & Operations Research, 39(3), 541
Oloruntoba, R. (2010). An analysis of the Cyclone Larry emergency relief chain: Some key success factors. International Journal of Production Economics,126(1), 101. Opit, P. F., Lee, W. S., Kim, B. S., & Nakade, K. (2013). Stock Pre-positioning
Model with Unsatisfied Relief Demand Constraint to Support Emergency, SHUDWLRQV $QG 6XSSO &KDıQ 0DQDJHPHQW, 6(2),103 - 110
Ortuño, M. T., Cristóbal, P., Ferrer, J. M., Martin-Campo, F. J., Muñoz, S., Tirado, G., & Vitoriano, B. (2013). Decision aid models and systems for humanitarian logistics. A survey. In Decision aid models for disaster management and emergencies (pp. 17-44). Atlantis Press.
Ortuño, M. T., Tirado, G., & Vitoriano, B. (2011). A lexicographical goal programming based decision support system for logistics of Humanitarian Aid. Top, (2), 464-479.
Ozbay, K., & Ozguven, E. E. (2007). Stochastic humanitarian inventory control model for disaster planning. Transportation Research Record: Journal of the Transportation Research Board, 2022(1), 63-75.
Ozbay, K., & Ozguven, E. E. (2007). Stochastic humanitarian inventory control model for disaster planning. Transportation Research Record: Journal of the Transportation Research Board, 2022(1), 63-75.
Ozdamar, L. (2011). Planning helicopter logistics in disaster relief. OR spectrum,
Özdamar, L., & Demir, O. (2012). A hierarchical clustering and routing procedure for large scale disaster relief logistics planning. Transportation Research Part E:
Logistics and Transportation Review, 48(3), 591-602. Özdamar, L., & Ertem, M. A. (2014). Models, solutions and enabling technologies in humanitarian logistics. European Journal of Operational Research. g]GDPDU / $NVX ' 7 (UJQHş % &RRUGLQDWLQJ GHEULV FOHDQXS operations in post disaster road networks. Socio-Economic Planning Sciences, 48(4), 262.
Ozguven, E. E., & Ozbay, K. (2012). Case study-based evaluation of stochastic multicommodity emergency inventory management model. Transportation Research Record: Journal of the Transportation Research Board, 2283(1), 12-24.
Ozguven, E. E., & Ozbay, K. (2013). A secure and efficient inventory management system for disasters. Transportation research part C: emerging technologies, 29, 196.
Paul, J. A., & Batta, R. (2008). Models for hospital location and capacity allocation for an area prone to natural disasters. International Journal of Operational Research, 3(5), 473-496.
Paul, J. A., & Hariharan, G. (2012). Location-allocation planning of stockpiles for effective disaster mitigation. Annals of operations research, 196(1), 469-490.
Pedraza-Martinez, A. J., & Van Wassenhove, L. N. (2012). Transportation and vehicle fleet management in humanitarian logistics: challenges for future research.
EURO Journal on Transportation and Logistics, 1(1-2), 185-196. Pedraza-Martinez, A. J., & Van Wassenhove, L. N. (2013). Vehicle replacement in the International Committee of the Red Cross. Production and operations Management, 22(2), 365-376.
Pedraza-Martinez, A. J., Stapleton, O., & Van Wassenhove, L. N. (2011). Field vehicle fleet management in humanitarian operations: A case-based approach. Journal of Operations Management, 29(5), 404-421. Peeta, S., Salman, F. S., Gunnec, D., & Viswanath, K. (2010). Pre-disaster investment decisions for strengthening a highway network. Computers & Operations Research, 37(10), 1708-1719.
Peng, M., Peng, Y., & Chen, H. (2014). Post-seismic supply chain risk management: A system dynamics disruption analysis approach for inventory and logistics planning. Computers & Operations Research, 42, 14-24. Peng, Y. (2012). Regional earthquake vulnerability assessment using a combination of MCDM methods. Annals of Operations Research, 1-16. http://dx.doi.org/10.1007/s10479-012-1253-8
Pérez-Rodríguez, N., & Holguín-Veras, J. (2015). Inventory-Allocation Distribution Models for Postdisaster Humanitarian Logistics with Explicit Consideration of Deprivation Costs. Transportation Science, http://dx.doi.org/10.1287/trsc.2014.0565.
Pettit, S., & Beresford, A. (2009). Critical success factors in the context of humanitarian aid supply chains. International Journal of Physical Distribution &
Logistics Management, 39(6), 450-468. Psaraftis, H. N., Tharakan, G. G., & Ceder, A. (1986). Optimal response to oil spills: the strategic decision case. Operations Research, 34(2), 203-217.
Rancourt, M. È., Cordeau, J. F., Laporte, G., & Watkins, B. (2015). Tactical network planning for food aid distribution in Kenya. Computers & Operations Research, 56, 68-83.
Ransikarbum, K., & Mason, S. J. (2014). Multiple-objective analysis of integrated relief supply and network restoration in humanitarian logistics operations. International Journal of Production Research, 1-20. http://dx.doi.org/10.1080/00207543.2014.977458
Rath, S., & Gutjahr, W. J. (2014). A math-heuristic for the warehouse location– routing problem in disaster relief. Computers & Operations Research, 42, 25-39.
Rath, S., Gendreau, M., & Gutjahr, W. J. (2015). Bi-objective stochastic programming models for determining depot locations in disaster relief operations.
International Transactions in Operational Research, http://dx.doi.org/10.1111/itor.12163
Rawls, C. G., & Turnquist, M. A. (2010). Pre-positioning of emergency supplies for disaster response. Transportation research part B: Methodological, 44(4), 521-534.
Rawls, C. G., & Turnquist, M. A. (2011). Pre-positioning planning for emergency response with service quality constraints. OR spectrum, 33(3), 481-498.
Rawls, C. G., & Turnquist, M. A. (2012). Pre-positioning and dynamic delivery planning for short-term response following a natural disaster. Socio-Economic
Planning Sciences, 46(1), 46-54. Renkli, Ç., & Duran, S. (2014). Pre-Positioning Disaster Response Facilities and Relief Items. Human and Ecological Risk Assessment: An International Journal,21(5), 1169-1185.
Rennemo, S. J., Rİ, K. F., Hvattum, L. M., & Tirado, G. (2014). A three-stage stochastic facility routing model for disaster response planning. Transportation
Research Part E: Logistics and Transportation Review, 62, 116-135. Reyes, P., Man, J., & Jaska, P. (2013). A disaster relief inventory model based on transshipment. Independent Journal of Management & Production, 4(2), 481-509.
Rivera, J. C., Afsar, H. M., & Prins, C. (2014, January). Multistart Evolutionary Local Search for a Disaster Relief Problem. In Artificial Evolution (pp. 129-141). Springer International Publishing. Rodríguez, J. T., Vitoriano, B., & Montero, J. (2010). A natural-disaster management DSS for Humanitarian Non-Governmental Organisations. Knowledge- Based Systems, 23(1), 17-22.
Roh, S., Pettit, S., Harris, I., & Beresford, A. (2015). The pre-positioning of warehouses at regional and local levels for a humanitarian relief organisation. International Journal of Production Economics, doi:10.1016/j.ijpe.2015.01.015
Rong, X. X., Lu, Y., Yin, R. R., & Zhang, J. H. (2013). A robust optimization approach to emergency vehicle scheduling. Mathematical Problems in Engineering,
Rottkemper, B., Fischer, K., & Blecken, A. (2012). A transshipment model for distribution and inventory relocation under uncertainty in humanitarian operations.
Socio-Economic Planning Sciences, 46(1), 98-109. Rottkemper, B., Fischer, K., Blecken, A., & Danne, C. (2011). Inventory relocation for overlapping disaster settings in humanitarian operations. OR spectrum, 33(3), 749.
Ruan, J., Wang, X., & Shi, Y. (2014). A two-stage approach for medical supplies intermodal transportation in large-scale disaster responses. International journal of environmental research and public health, 11(11), 11081-11109.
Salas, L. C., Cárdenas, M. R., & Zhang, M. (2012). Inventory policies for humanitarian aid during hurricanes. Socio-Economic Planning Sciences, 46(4), 272
Salman, F. S., & Gül, S. (2014). Deployment of field hospitals in mass casualty incidents. Computers & Industrial Engineering, 74, 37-51.
Salman, F. S., & Yücel, E. (2014). Emergency facility location under random network damage: Insights from the Istanbul case. Computers & Operations
Research, http://dx.doi.org/10.1016/j.cor.2014.07.015
Salmerón, J., & Apte, A. (2010). Stochastic optimization for natural disaster asset prepositioning. Production and Operations Management, 19(5), 561-574.
Shen, Z., Dessouky, M. M., & Ordóñez, F. (2009). A two-stage vehicle routing model for large-scale bioterrorism emergencies. Networks, 54(4), 255-269.
Shen, Z., Dessouky, M. M., & Ordóñez, F. (2009). A two-stage vehicle routing model for large-scale bioterrorism emergencies. Networks, 54(4), 255-269.
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.
Sheu, J. B. (2007). Challenges of emergency logistics management. Transportation
Research part E: Logistics and Transportation Review, 43(6), 655-659. 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.
Sheu, J. B. (2014). Post-disaster relief–service centralized logistics distribution with survivor resilience maximization. Transportation research part B: methodological, , 288-314.
Sheu, J. B., & Pan, C. (2014). A method for designing centralized emergency supply network to respond to large-scale natural disasters. Transportation research part B: methodological, 67, 284-305.
Sheu, J. B., & Pan, C. (2014). Relief supply collaboration for emergency logistics responses to large-scale disasters. Transportmetrica A: Transport Science, 11(3), 242.
Shui, W., Ye, H., Zhao, J., & Liu, M. (2008). A Dynamic Multiple Objective Model of Location Problem of Emergency Logistics Distribution Centers. In Logistics@ sThe Emerging Frontiers of Transportation and Development in China (pp. 929- ). ASCE.
Su, Q., Luo, Q., & Huang, S. H. (2015). Cost-effective analyses for emergency medical services deployment: A case study in Shanghai. International Journal of
Production Economics, 163, 112-123. Sun, B., Ma, W., & Zhao, H. (2013). A fuzzy rough set approach to emergency material demand prediction over two universes. Applied Mathematical Modelling, (10), 7062-7070.
Talarico, L., Meisel, F., & Sörensen, K. (2015). Ambulance routing for disaster response with patient groups. Computers & Operations Research, 56, 120-133.
Taskin, S., & Lodree, E. J. (2010). Inventory decisions for emergency supplies based on hurricane count predictions. International Journal of Production Economics, 126(1), 66-75.
Tirado, G., Martín-Campo, F. J., Vitoriano, B., & Ortuño, M. T. (2014). A lexicographical dynamic flow model for relief operations. International Journal of
Computational Intelligence Systems, 7(sup1), 45-57. Toregas, C., Swain, R., ReVelle, C., & Bergman, L. (1971). The location of emergency service facilities. Operations Research, 19(6), 1363-1373.
Trestrail, J., Paul, J., & Maloni, M. (2009). Improving bid pricing for humanitarian logistics. International Journal of Physical Distribution & Logistics Management, (5), 428-441.
Turgut, B,T., Tas, G., Herekoglu, A., Tozan, H., & Vayvay, O. (2011). A fuzzy
AHP based decision support system for disaster center location selection and a case study for Istanbul. Disaster Prevention and Management: An International Journal, (5), 499-520. 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. Ukkusuri, S. V., & Yushimito, W. F. (2008). Location routing approach for the humanitarian prepositioning problem. Transportation Research Record: Journal of the Transportation Research Board, 2089(1), 18-25.
Van Hentenryck, P., Bent, R., & Coffrin, C. (2010). Strategic planning for disaster recovery with stochastic last mile distribution. In Integration of AI and OR techniques in constraint programming for combinatorial optimization problems (pp. 333). Springer Berlin Heidelberg.
Van Wassenhove, L. N., & Pedraza Martinez, A. J. (2012). Using OR to adapt supply chain management best practices to humanitarian logistics. International
Transactions in Operational Research, 19(1-2), 307-322. Verma, A., & Gaukler, G. M. (2011). A stochastic optimization model for positioning disaster response facilities for large scale emergencies. In Network
Optimization (pp. 547-552). Springer Berlin Heidelberg. Verma, A., & Gaukler, G. M. (2014). Pre-positioning disaster response facilities at safe locations: An evaluation of deterministic and stochastic modeling approaches. Computers & Operations Research, http://dx.doi.org/10.1016/j.cor.2014.10.006.
Verma, M., Gendreau, M., & Laporte, G. (2013). Optimal location and capability of oil-spill response facilities for the south coast of Newfoundland. Omega,41(5), 856
Vitoriano, B., 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.
Vitoriano, B., Ortuno, T., & Tirado, G. (2009). HADS, a goal programming-based humanitarian aid distribution system. Journal of Multi-Criteria Decision Analysis, (1-2), 55-64.
Vitoriano, B., Rodríguez, J. T., Tirado, G., Martín-Campo, F. J., Ortuño, M. T., & Montero, J. (2015). Intelligent Decision-Making Models for Disaster Management. Human and Ecological Risk Assessment: An International Journal, 21(5), 1341
Wang, H., Du, L., & Ma, S. (2014). Multi-objective open location-routing model with split delivery for optimized relief distribution in post-earthquake.
Transportation Research Part E: Logistics and Transportation Review, 69, 160-179. Wang, X., Wu, Y., Liang, L., & Huang, Z. (2014). Service outsourcing and disaster response methods in a relief supply chain. Annals of Operations Research, 1-17. http://dx.doi.org/10.1007/s10479-014-1646-y
Wex, F., Schryen, G., Feuerriegel, S., & Neumann, D. (2014). Emergency response in natural disaster management: Allocation and scheduling of rescue units.
European Journal of Operational Research, 235(3), 697-708. Whybark, D. C. (2007). Issues in managing disaster relief inventories. International
Journal of Production Economics, 108(1), 228-235. Widener, M. J., & Horner, M. W. (2011). A hierarchical approach to modeling hurricane disaster relief goods distribution. Journal of Transport Geography, 19(4), 828.
Wilson, D. T., Hawe, G. I., Coates, G., & Crouch, R. S. (2013). A multi-objective combinatorial model of casualty processing in major incident response. European
Journal of Operational Research, 230(3), 643-655. Wilson, D. T., Hawe, G. I., Coates, G., & Crouch, R. S. (2014). Evaluation of centralised and autonomous routing strategies in major incident response.Safety science, 70, 80-88.
Wohlgemuth, S., Oloruntoba, R., & Clausen, U. (2012). Dynamic vehicle routing with anticipation in disaster relief. Socio-Economic Planning Sciences, 46(4), 261
Xu, X., Qi, Y., & Hua, Z. (2010). Forecasting demand of commodities after natural disasters. Expert Systems with Applications, 37(6), 4313-4317.
Yadavalli, V. S. S., Sundar, D. K., & Udayabaskaran, S. (2015). Two substitutable perishable product disaster inventory systems. Annals of Operations Research, 1-18. http://dx.doi.org/10.1007/s10479-014-1783-3
Yan, S., & Shih, Y. L. (2009). Optimal scheduling of emergency roadway repair and subsequent relief distribution. Computers & Operations Research, 36(6), 2049
Yang, F., Yuan, Q., Du, S., & Liang, L. (2014). Reserving relief supplies for earthquake: a multi-attribute decision making of China Red Cross. Annals of Operations Research, 1-27. http://dx.doi.org/10.1007/s10479-014-1749-5
Yi, P., George, S. K., Paul, J. A., & Lin, L. (2010). Hospital capacity planning for disaster emergency management. Socio-Economic Planning Sciences, 44(3), 151 Yi, W., & Kumar, A. (2007). Ant colony optimization for disaster relief operations.
Transportation Research Part E: Logistics and Transportation Review, 43(6), 660- 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.
Yuan, Y., & Wang, D. (2009). Path selection model and algorithm for emergency logistics management. Computers & Industrial Engineering, 56(3), 1081-1094.
Yushimito, W. F., Jaller, M., & Ukkusuri, S. (2012). A Voronoi-based heuristic algorithm for locating distribution centers in disasters. Networks and Spatial Economics, 12(1), 21-39.
Zhan, S. L., & Liu, N. (2014). Determining the optimal decision time of relief allocation in response to disaster via relief demand updates. International Journal of Systems Science,1-12, http://dx.doi.org/10.1080/00207721.2014.891665
Zhan, S. L., Liu, N., & Ye, Y. (2014). Coordinating efficiency and equity in disaster relief logistics via information updates. International Journal of Systems Science,
Zhang, J. H., Li, J., & Liu, Z. P. (2012). Multiple-resource and multiple-depot emergency response problem considering secondary disasters. Expert Systems with Applications, 39(12), 11066-11071.
Zhang, M. X., Zhang, B., & Zheng, Y. J. (2014). Bio-Inspired Meta-Heuristics for Emergency Transportation Problems. Algorithms, 7(1), 15-31. Zhang, X., Zhang, Z., Zhang, Y., Wei, D., & Deng, Y. (2013). Route selection for emergency logistics management: a bio-inspired algorithm. Safety science, 54, 87
Zheng, Y. J., & Ling, H. F. (2013). Emergency transportation planning in disaster relief supply chain management: a cooperative fuzzy optimization approach. Soft Computing, 17(7), 1301-1314.
Zheng, Y. J., Chen, S. Y., & Ling, H. F. (2015). Evolutionary optimization for disaster relief operations: a survey. Applied Soft Computing, 27, 553-566.
Zhou, Q., Huang, W., & Zhang, Y. (2011). Identifying critical success factors in emergency management using a fuzzy DEMATEL method. Safety Science,49(2), 252.
Zidi, K., Mguis, F., Borne, P., & Ghedira, K. (2013). Distributed genetic algorithm for disaster relief planning. International Journal of Computers Communications & Control, 8(5), 769-783.