Managing the surge in demand for blood following mass casualty events. Early automatic restocking may preserve red cell supply

Background: Traumatic hemorrhage is a leading preventable cause of mortality following mass casualty events (MCEs). Improving outcomes requires adequate in-hospital provision of high volume red blood cell (RBC) transfusions. This study investigated strategies for optimizing RBC provision to casualties in MCEs using simulation modeling. Methods: A computerized simulation model of a UK major trauma centre (TC) transfusion system was developed. The model used input data from past MCEs, civilian and military trauma registries. We simulated the effect of varying on-shelf RBC stock hold and the timing of externally restocking RBC supplies on TC treatment capacity across increasing loads of priority one (P1) and two (P2) casualties from an event. Results: 35,000 simulations were performed. A casualty load of 20 P1&2s under standard TC RBC stock conditions left 35% (95% CI 32-38) of P1s and 7% (4-10) of P2s inadequately treated for hemorrhage. Additionally, exhaustion of type O emergency RBC stocks (a surrogate for reaching surge capacity) occurred in a median of 10 hours (IQR 5->12). Doubling casualty load increased this to 60% (57-63) and 30% (26-34) respectively with capacity reached in 2hours (1-3). The model identified a minimum requirement of 12U of on-shelf RBCs per P1/2 casualty received to prevent surge capacity being reached. Restocking supplies in an MCE versus greater permanent on-shelf RBC stock holds was considered at increasing hourly intervals. T-test analysis showed no difference between stock hold versus supply restocking in terms of overall outcomes for MCEs up to 80 P1&2s in size (p<0.05), provided the restock occurred within 6 hours. Conclusion: Even limited sized MCEs threaten to overwhelm TC transfusion systems. An earlyautomated push approach to restocking RBCs initiated by central suppliers can produce equivocal outcomes compared with holding excess stock permanently at TCs

Disaster management from a POM perspective : mapping a new domain

We have reviewed disaster management research papers published in major operations management, management science, operations research, supply chain management and transportation/ logistics journals. In reviewing these papers our objective is to assess and present the macro level “architectural blue print” of disaster management research with the hope that it will attract new researchers and motivate established researchers to contribute to this important field. The secondary objective is to bring this disaster research to the attention of disaster administrators so that disasters are managed more efficiently and more effectively. We have mapped the disaster management research on the following five attributes of a disaster: (1) Disaster Management Function (decision making process, prevention and mitigation, evacuation, humanitarian logistics, casualty management, and recovery and restoration), (2) Time of Disaster (before, during and after), (3) Type of Disaster (accidents, earthquakes, floods, hurricanes, landslides, terrorism and wildfires etc.), (4) Data Type (Field and Archival data, Real data and Hypothetical data), and (5) Data Analysis Technique (bidding models, decision analysis, expert systems, fuzzy system analysis, game theory, heuristics, mathematical programming, network flow models, queuing theory, simulation and statistical analysis). We have done cross tabulations of data among these five parameters to gain greater insights in disaster research. Recommendations for future research are provided

PRIORITIZATION AND DISTRIBUTION OF CASUALTIES IN DISASTER RESPONSE MANAGEMENT

This dissertation focused on two different problems that typically arise in the aftermath of disasters. In the first part of the dissertation, we study the problem of how casualties should be prioritized and distributed to different medical facilities in the aftermath of mass casualty incidents (MCIs) with the objective of maximizing the expected total number of survivors. Assuming that casualties have been triaged into two classes differentiated by their severity levels and medical needs, the decision-maker needs to prioritize and distribute casualties using a limited number of ambulances to multiple medical facilities with different capacities. By explicitly taking into consideration the capacity and service time at each medical facility, we formulate this sequential decision-making problem as a Markov decision process (MDP). Based on this MDP formulation, we propose heuristic policies that prescribe decisions on prioritization and distribution of casualties. We then employ discrete-event simulations to demonstrate the benefits of using the proposed heuristics against some benchmark policies under several realistic mass casualty incident scenarios such as terrorist attacks, major traffic accidents, and earthquakes. In the second part of the dissertation, we study the resource allocation problem in urban search and rescue operations that follow natural disasters. Specifically, we consider a scenario in which some individuals are trapped at various locations within a geographical area and there is a limited time window during which these individuals can be rescued. We model the problem as an MDP. Then, we characterize the optimal policy under the assumption that individuals belong to only one of two locations. We propose heuristics for the general version of the problem. Finally, the proposed heuristics are examined with a simulation.Doctor of Philosoph

Community Responses to Mass Casualty Events: A Mixed Method Approach

This dissertation explores how traits of a mass casualty event and community institutionalization affect how a community demonstrates solidarity after a mass casualty event. A systematic examination of mass casualty events along these lines has not been conducted before. Theoretically, individual helping behaviors like altruism help explain individual involvement in demonstrations of solidarity while solidarity and resilience help in explaining group behaviors. A typology is proposed that breaks up mass casualty events into four different types: terrorism, criminal, weather and accidents. These types of events make up the majority of non-war mass casualty events. Experimentally a sample of students is used to assess how individuals are likely to respond to mass casualty events by gauging how they would respond using five different types of demonstrations of solidarity. Findings suggest that victim type positively influences demonstrations of solidarity while casualty number and event type are only selectively influential. Two cases (Orlando, FL 2016 and San Bernardino, CA 2015) are used to test three hypotheses that are related to how a community demonstrates solidarity after a mass casualty event. Results indicate that victim type positively influences demonstrations of solidarity, particularly through the specific institutions within vulnerable communities that increased access to demonstrations. Additionally, increased institutionalization within the victim community also positively influences demonstrations of solidarity. Furthermore, results suggest that event specific traits do influence demonstrations of solidarity under certain circumstances. However, more empirical research is needed to examine how individuals respond and the exact processes available to communities that would aid in their recovery from such an event

Optimization Models for Last-Mile Service Operations

Improving the quality of the last-mile service that comprises the movement of goods and people has been a recurrent theme in recent research. This dissertation aims to develop optimization models for addressing the emerging challenges encountered by three different last-mile service systems in the domains of inventory-delivery management, transportation and logistics, and emergency medical services, respectively. The first chapter focuses on the domain of inventory-delivery management by investigating a two-echelon, single-product fulfillment system, in which the expected system-wide demand depends on the product’s price, the committed delivery time, and the number of local distribution centers in the system. The proposed model maximizes the expected profit while accounting for the total expected revenue, product holding costs, and fixed facility costs. The second chapter concentrates on the domain of transportation and logistics, in which a fleet composition problem is studied. The proposed models consider using both internal truckload capacity and external less-than-truckload shipments for fulfilling stochastic demand. The objective is to minimize the total expected cost per period while determining the number of trucks to own for each truck type given a set of truck classes. The third chapter contributes to the field of emergency medical services by studying a routing problem in the aftermath of a disaster, wherein the proposed models aim to determine the optimal routing strategy while maximizing the total expected number of survivors. A single ambulance bus is used to transport a given number of casualties to a medical center, accounting for time-dependent survival probabilities of the casualties. The models and solution approaches developed in this dissertation provide managerial insights and can serve as a starting point when making decisions on supply chain design, facility locating, asset procurement, and fleet management and operations

Modelling Red Blood Cell Provision in Mass Casualty Events

PhDTraumatic haemorrhage is a leading preventable cause of critical mortality in mass casualty events (MCEs). Treatment requires the rapid provision of high volumes of packed red blood cells (PRBC) to meet the surge in casualty demand these events generate. The increasing frequency of MCEs coupled with the threat of more violent mechanisms risks overwhelming hospital based transfusion systems. The overall objective of this research was to improve understanding of blood use in MCEs using a mathematical modelling approach. A computerised discrete event simulation model was designed, developed and validated using civilian and military transfusion databases, a review of historical MCEs and discussion with experts involved in all aspects of in-hospital MCE PRBC provision. The model was experimented with across increasing casualty loads to optimise event outcomes under varied conditions of: stock availability, laboratory processing procedures and individual PRBC supply. The model indicated even in events of limited size the standard on-shelf PRBC stock level was insufficient to adequately meet demand amongst bleeding casualties. Restocking during an event allowed for equivocal treatment results if performed early following an event and this would be most effective if activated by central suppliers. Modifications to transfusion laboratory processing procedures were found to be of limited benefit in improving outcomes due to the principally automated nature of the techniques they employ. Conversely, the use of restricting excessive individual provision of both overall PRBC and emergency type O PRBC to individual casualties did show potential for managing scenarios where only a finite supply of stock existed or an accurate estimation of expected casualties was available

Mass Casualty Management in Disaster Scene: A Systematic Review of OR&MS research in Humanitarian Operations

[EN] Disasters are usually managed through a four-phase cycle including mitigation, preparedness, response and recovery. The first two phases happen before a disaster and the last two after it. This survey focuses on casualty management (CM), which is one of the actions taken in the response phase of a disaster. Right after a severe disaster strikes, we may be confronted with a large number of casualties in a very short period of time. These casualties are in need of urgent treatment and their survival depends on a rapid response. Therefore, managing resources in the first few hours after a disaster is critical and efficient CM can significantly increase the survival rate of casualties. Uncertainty in the location of a disaster, disruption to transportation networks, scarcity of resources and possible deaths of rescue and medical teams due to the disaster in such situations make it hard to manage casualties. In this survey, we focus on CM for disasters where the following five steps are taken, respectively: (i) Resource dispatching/search and rescue, (ii) on-site triage, (iii) on-site medical assistance, (iv) transportation to hospitals and (v) triage and comprehensive treatment. With a special focus on Operations Research (OR) techniques, we categorize the existing research papers and case studies in each of these steps. Then, by critically observing and investigating gaps, trends and the practicality of the extant research studies, we suggest future directions for academics and practitioners.Ruben Ruiz is partially supported by the Spanish Ministry of Science, Innovation, and Universities, under the project "OPTEP-Port Terminal Operations Optimization"(No. RTI2018-094940-B-I00) financed with FEDER funds.Farahani, RZ.; Lotfi, MM.; Baghaian, A.; Ruiz García, R.; Rezapour, S. (2020). Mass Casualty Management in Disaster Scene: A Systematic Review of OR&MS research in Humanitarian Operations. European Journal of Operational Research. 287(3):787-819. https://doi.org/10.1016/j.ejor.2020.03.005S7878192873Abdelgawad, H., & Abdulhai, B. (2009). Emergency evacuation planning as a network design problem: a critical review. Transportation Letters, 1(1), 41-58. doi:10.3328/tl.2009.01.01.41-58Abidi, H., de Leeuw, S., & Klumpp, M. (2014). Humanitarian supply chain performance management: a systematic literature review. Supply Chain Management: An International Journal, 19(5/6), 592-608. doi:10.1108/scm-09-2013-0349Al Theeb, N., & Murray, C. (2016). Vehicle routing and resource distribution in postdisaster humanitarian relief operations. International Transactions in Operational Research, 24(6), 1253-1284. doi:10.1111/itor.12308Alinaghian, M., Aghaie, M., & Sabbagh, M. S. (2019). A mathematical model for location of temporary relief centers and dynamic routing of aerial rescue vehicles. Computers & Industrial Engineering, 131, 227-241. doi:10.1016/j.cie.2019.03.002Alizadeh, M., Amiri-Aref, M., Mustafee, N., & Matilal, S. (2019). A robust stochastic Casualty Collection Points location problem. European Journal of Operational Research, 279(3), 965-983. doi:10.1016/j.ejor.2019.06.018Al-Momani, N. M., & Harrald, J. R. (2003). Sensitivity of earthquake loss estimation model: how useful are the predictions. International Journal of Risk Assessment and Management, 4(1), 1. doi:10.1504/ijram.2003.003433Alotaibi, E. T., Alqefari, S. S., & Koubaa, A. (2019). LSAR: Multi-UAV Collaboration for Search and Rescue Missions. IEEE Access, 7, 55817-55832. doi:10.1109/access.2019.2912306Altay, N., & Green, W. G. (2006). OR/MS research in disaster operations management. European Journal of Operational Research, 175(1), 475-493. doi:10.1016/j.ejor.2005.05.016Esposito Amideo, A., Scaparra, M. P., & Kotiadis, K. (2019). Optimising shelter location and evacuation routing operations: The critical issues. European Journal of Operational Research, 279(2), 279-295. doi:10.1016/j.ejor.2018.12.009Anaya-Arenas, A. M., Renaud, J., & Ruiz, A. (2014). Relief distribution networks: a systematic review. Annals of Operations Research, 223(1), 53-79. doi:10.1007/s10479-014-1581-yApte, A., Heidtke, C., & Salmerón, J. (2015). Casualty Collection Points Optimization: A Study for the District of Columbia. Interfaces, 45(2), 149-165. doi:10.1287/inte.2014.0757Baharmand, H., Comes, T., & Lauras, M. (2019). Bi-objective multi-layer location–allocation model for the immediate aftermath of sudden-onset disasters. Transportation Research Part E: Logistics and Transportation Review, 127, 86-110. doi:10.1016/j.tre.2019.05.002Balcik, B., Beamon, B. M., & Smilowitz, K. (2008). Last Mile Distribution in Humanitarian Relief. Journal of Intelligent Transportation Systems, 12(2), 51-63. doi:10.1080/15472450802023329Balcik, B., Bozkir, C. D. C., & Kundakcioglu, O. E. (2016). A literature review on inventory management in humanitarian supply chains. Surveys in Operations Research and Management Science, 21(2), 101-116. doi:10.1016/j.sorms.2016.10.002Behl, A., & Dutta, P. (2018). Humanitarian supply chain management: a thematic literature review and future directions of research. Annals of Operations Research, 283(1-2), 1001-1044. doi:10.1007/s10479-018-2806-2Berkoune, D., Renaud, J., Rekik, M., & Ruiz, A. (2012). Transportation in disaster response operations. Socio-Economic Planning Sciences, 46(1), 23-32. doi:10.1016/j.seps.2011.05.002Besiou, M., Pedraza-Martinez, A. J., & Van Wassenhove, L. N. (2018). OR applied to humanitarian operations. European Journal of Operational Research, 269(2), 397-405. doi:10.1016/j.ejor.2018.02.046Boonmee, C., Arimura, M., & Asada, T. (2017). Facility location optimization model for emergency humanitarian logistics. International Journal of Disaster Risk Reduction, 24, 485-498. doi:10.1016/j.ijdrr.2017.01.017Bravo, R. Z. B., Leiras, A., & Cyrino Oliveira, F. L. (2018). The Use of UAVs in Humanitarian Relief: An Application of POMDP-Based Methodology for Finding Victims. Production and Operations Management, 28(2), 421-440. doi:10.1111/poms.12930California Office of Statewide Health Planning & Development, 2016. Available online on http://oshpd.ca.gov/, Visited on 28 Nov (2016).Canes, M. (2015). Mais de 10 mil pessoas foram atingidas por tornado em Xanxerê, Available online onhttp://agenciabrasil.ebc.com.br/geral/noticia/2015-04/mais-de-10-mil-pessoas-foram-atingidas-por-tornado-em-xanxere, Visited on 20 Jan 2017).Çankaya, E., Ekici, A., & Özener, O. Ö. (2018). Humanitarian relief supplies distribution: an application of inventory routing problem. Annals of Operations Research, 283(1-2), 119-141. doi:10.1007/s10479-018-2781-7Cao, C., Li, C., Yang, Q., Liu, Y., & Qu, T. (2018). A novel multi-objective programming model of relief distribution for sustainable disaster supply chain in large-scale natural disasters. Journal of Cleaner Production, 174, 1422-1435. doi:10.1016/j.jclepro.2017.11.037Caunhye, 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. doi:10.1016/j.seps.2015.02.001Caunhye, A. M., & Nie, X. (2018). A Stochastic Programming Model for Casualty Response Planning During Catastrophic Health Events. Transportation Science, 52(2), 437-453. doi:10.1287/trsc.2017.0777Caunhye, A. M., Nie, X., & Pokharel, S. (2012). Optimization models in emergency logistics: A literature review. Socio-Economic Planning Sciences, 46(1), 4-13. doi:10.1016/j.seps.2011.04.004Chan, C. W., Green, L. V., Lu, Y., Leahy, N., & Yurt, R. (2013). Prioritizing Burn-Injured Patients During a Disaster. Manufacturing & Service Operations Management, 15(2), 170-190. doi:10.1287/msom.1120.0412Chen, L., & Miller-Hooks, E. (2012). Optimal team deployment in urban search and rescue. Transportation Research Part B: Methodological, 46(8), 984-999. doi:10.1016/j.trb.2012.03.004Christie, P. M. J., & Levary, R. R. (1998). Journal of Medical Systems, 22(5), 289-300. doi:10.1023/a:1020521909778Cohen, I., Mandelbaum, A., & Zychlinski, N. (2014). Minimizing mortality in a mass casualty event: fluid networks in support of modeling and staffing. IIE Transactions, 46(7), 728-741. doi:10.1080/0740817x.2013.855846Cook, W. D. (1977). A Model for the Evacuation of Casualties from a Field Hospital During a Crisis. Journal of the Operational Research Society, 28(4), 963-974. doi:10.1057/jors.1977.194Cotta, C. (2011). Effective patient prioritization in mass casualty incidents using hyperheuristics and the pilot method. OR Spectrum, 33(3), 699-720. doi:10.1007/s00291-011-0238-3CRED (Centre for Research on the Epidemiology of Disasters) (2018). Cred Crunch 52 - Economic Losses, Poverty and Disasters: 1998-2017. Available at www.cred.be/sites/default/files/CredCrunch52.pdf.Davoodi, S. M. R., & Goli, A. (2019). An integrated disaster relief model based on covering tour using hybrid Benders decomposition and variable neighborhood search: Application in the Iranian context. Computers & Industrial Engineering, 130, 370-380. doi:10.1016/j.cie.2019.02.040De la Torre, L. E., Dolinskaya, I. S., & Smilowitz, K. R. (2012). Disaster relief routing: Integrating research and practice. Socio-Economic Planning Sciences, 46(1), 88-97. doi:10.1016/j.seps.2011.06.001Dean, M. D., & Nair, S. K. (2014). Mass-casualty triage: Distribution of victims to multiple hospitals using the SAVE model. European Journal of Operational Research, 238(1), 363-373. doi:10.1016/j.ejor.2014.03.028Debacker, M., Van Utterbeeck, F., Ullrich, C., Dhondt, E., & Hubloue, I. (2016). SIMEDIS: a Discrete-Event Simulation Model for Testing Responses to Mass Casualty Incidents. Journal of Medical Systems, 40(12). doi:10.1007/s10916-016-0633-zDepartment of Homeland Security. National planning scenarios: Executive summaries. (2005)., Available athttp://cees.tamiu.edu/covertheborder/TOOLS/NationalPlanningSen.pdf(April 2005).Doan, X. V., & Shaw, D. (2019). Resource allocation when planning for simultaneous disasters. European Journal of Operational Research, 274(2), 687-709. doi:10.1016/j.ejor.2018.10.015Drezner, T. (2004). Location of Casualty Collection Points. Environment and Planning C: Government and Policy, 22(6), 899-912. doi:10.1068/c13rEdrissi, A., Nourinejad, M., & Roorda, M. J. (2015). Transportation network reliability in emergency response. Transportation Research Part E: Logistics and Transportation Review, 80, 56-73. doi:10.1016/j.tre.2015.05.005Edrissi, 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. doi:10.1016/j.ejor.2013.03.008EM-DAT, Natural Disasters (2017). Available atwww.cred.be/sites/default/fles/adsr_2017.pdf.FEMA. National Incident Management System. (2008)., Available online onwww.fema.gov/media-library/assets/documents/25422, Visited on 11 Nov 2018.Fiedrich, F., Gehbauer, F., & Rickers, U. (2000). Optimized resource allocation for emergency response after earthquake disasters. Safety Science, 35(1-3), 41-57. doi:10.1016/s0925-7535(00)00021-7Franco, C., Toner, E., Waldhorn, R., Maldin, B., O’Toole, T., & Inglesby, T. V. (2006). Systemic Collapse: Medical Care in the Aftermath of Hurricane Katrina. Biosecurity and Bioterrorism: Biodefense Strategy, Practice, and Science, 4(2), 135-146. doi:10.1089/bsp.2006.4.135Frykberg, E. R. (2005). Triage: Principles and Practice. Scandinavian Journal of Surgery, 94(4), 272-278. doi:10.1177/145749690509400405Galindo, G., & Batta, R. (2013). Review of recent developments in OR/MS research in disaster operations management. European Journal of Operational Research, 230(2), 201-211. doi:10.1016/j.ejor.2013.01.039Ghasemi, P., Khalili-Damghani, K., Hafezalkotob, A., & Raissi, S. (2019). Uncertain multi-objective multi-commodity multi-period multi-vehicle location-allocation model for earthquake evacuation planning. Applied Mathematics and Computation, 350, 105-132. doi:10.1016/j.amc.2018.12.061Gong, Q., & Batta, R. (2007). Allocation and reallocation of ambulances to casualty clusters in a disaster relief operation. IIE Transactions, 39(1), 27-39. doi:10.1080/07408170600743938GPO Access Reports (2005). Chapter 24: Medical assistance. Available atwww.gpoaccess.gov/serialset/creports/pdf/sr109-322/ch24.pdfVisited on 30 Aug 2010.Gu, J., Zhou, Y., Das, A., Moon, I., & Lee, G. M. (2018). Medical relief shelter location problem with patient severity under a limited relief budget. Computers & Industrial Engineering, 125, 720-728. doi:10.1016/j.cie.2018.03.027Gupta, S., Starr, M. K., Farahani, R. Z., & Matinrad, N. (2016). Disaster Management from a POM Perspective: Mapping a New Domain. Production and Operations Management, 25(10), 1611-1637. doi:10.1111/poms.12591Gutjahr, W. J., & Nolz, P. C. (2016). Multicriteria optimization in humanitarian aid. European Journal of Operational Research, 252(2), 351-366. doi:10.1016/j.ejor.2015.12.035Haghi, M., Fatemi Ghomi, S. M. T., & Jolai, F. (2017). Developing a robust multi-objective model for pre/post disaster times under uncertainty in demand and resource. Journal of Cleaner Production, 154, 188-202. doi:10.1016/j.jclepro.2017.03.102Hallak, J., Koyuncu, M., & Miç, P. (2019). Determining shelter locations in conflict areas by multiobjective modeling: A case study in northern Syria. International Journal of Disaster Risk Reduction, 38, 101202. doi:10.1016/j.ijdrr.2019.101202Hamp, Q., Zhang, R., Chen, L., Gorgis, O., Ostertag, T., Loschonsky, M., & Reindl, L. (2014). New technologies for the search of trapped victims. Ad Hoc Networks, 13, 69-82. doi:10.1016/j.adhoc.2012.06.005Hoyos, M. C., Morales, R. S., & Akhavan-Tabatabaei, R. (2015). OR models with stochastic components in disaster operations management: A literature survey. Computers & Industrial Engineering, 82, 183-197. doi:10.1016/j.cie.2014.11.025Hu, X., Liu, Z., Yao, Y., Wang, N., & Dang, D. (2017). Crowdsourcing Model Research for the Identification of Post-Earthquake Rescue Objects. Journal of Earthquake Engineering, 23(5), 863-881. doi:10.1080/13632469.2017.1342304Huang, 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. doi:10.1016/j.tre.2014.11.007Jacobson, E. U., Argon, N. T., & Ziya, S. (2012). Priority Assignment in Emergency Response. Operations Research, 60(4), 813-832. doi:10.1287/opre.1120.1075Jin, 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, 230(1), 17-33. doi:10.1007/s10479-013-1515-0Jones, L.M., .Bernknopf, R., Cox, D., Goltz, J., Hudnut, K., Mileti, D. et al. (2008). The ShakeOut scenario. U.S. Geological Survey and California Geological Survey, Reston, VA, http://pubs.usgs.gov/of/2008/1150/of2008-1150small.pdf.Jotshi, A., Gong, Q., & Batta, R. (2009). Dispatching and routing of emergency vehicles in disaster mitigation using data fusion. Socio-Economic Planning Sciences, 43(1), 1-24. doi:10.1016/j.seps.2008.02.005Kamali, B., Bish, D., & Glick, R. (2017). Optimal service order for mass-casualty incident response. European Journal of Operational Research, 261(1), 355-367. doi:10.1016/j.ejor.2017.01.047Kang, J., Jeong, I.-J., & Kwun, J.-B. (2015). Optimal facility–final exit assignment algorithm for building complex evacuation. Computers & Industrial Engineering, 85, 169-176. doi:10.1016/j.cie.2015.03.012Karatas, M., Razi, N., & Gunal, M. M. (2017). An ILP and simulation model to optimize search and rescue helicopter operations. Journal of the Operational Research Society, 68(11), 1335-1351. doi:10.1057/s41274-016-0154-7Kilic, A., Dincer, M. C., & Gokce, M. A. (2014). Determining optimal treatment rate after a disaster. Journal of the Operational Research Society, 65(7), 1053-1067. doi:10.1057/jors.2013.52Kim, S., Shin, Y., Lee, G. M., & Moon, I. (2018). Early stage response problem for post-disaster incidents. Engineering Optimization, 50(7), 1198-1211. doi:10.1080/0305215x.2017.1419345Klein, K. R., & Nagel, N. E. (2007). Mass Medical Evacuation: Hurricane Katrina and Nursing Experiences at the New Orleans Airport. Disaster Management & Response, 5(2), 56-61. doi:10.1016/j.dmr.2007.03.001Kovacs, G., & Moshtari, M. (2019). A roadmap for higher research quality in humanitarian operations: A methodological perspective. European Journal of Operational Research, 276(2), 395-408. doi:10.1016/j.ejor.2018.07.052Lee, E. K., Chen, C.-H., Pietz, F., & Benecke, B. (2009). Modeling and Optimizing the Public-Health Infrastructure for Emergency Response. Interfaces, 39(5), 476-490. doi:10.1287/inte.1090.0463Leiras, 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 Management, 4(1), 95-130. doi:10.1108/jhlscm-04-2012-0008Lerner, E. B., Schwartz, R. B., Coule, P. L., Weinstein, E. S., Cone, D. C., Hunt, R. C., … O’Connor, R. E. (2008). Mass Casualty Triage: An Evaluation of the Data and Development of a Proposed National Guideline. Disaster Medicine and Public Health Preparedness, 2(S1), S25-S34. doi:10.1097/dmp.0b013e318182194eLi, D., & Glazebrook, K. D. (2010). An approximate dynamic programing approach to the development of heuristics for the scheduling of impatient jobs in a clearing system. Naval Research Logistics (NRL), 57(3), 225-236. doi:10.1002/nav.20395Li, M.-Y., Zhao, X.-J., Fan, Z.-P., Cao, P.-P., & Qu, X.-N. (2019). A model for assignment of rescuers considering multiple disaster areas. International Journal of Disaster Risk Reduction, 38, 101201. doi:10.1016/j.ijdrr.2019.101201Li, Y., & Chung, S. H. (2018). Disaster relief routing under uncertainty: A robust optimization approach. IISE Transactions, 51(8), 869-886. doi:10.1080/24725854.2018.1450540Liu, Y., Cui, N., & Zhang, J. (2019). Integrated temporary facility location and casualty allocation planning for post-disaster humanitarian medical service. Transportation Research Part E: Logistics and Transportation Review, 128, 1-16. doi:10.1016/j.tre.2019.05.008Liu, Y., Lei, H., Wu, Z., & Zhang, D. (2019). A robust model predictive control approach for post-disaster relief distribution. Computers & Industrial Engineering, 135, 1253-1270. doi:10.1016/j.cie.2018.09.005Lodree, E. J., Altay, N., & Cook, R. A. (2017). Staff assignment policies for a mass casualty event queuing network. Annals of Operations Research, 283(1-2), 411-442. doi:10.1007/s10479-017-2635-8Mahootchi, M., & Golmohammadi, S. (2017). Developing a new stochastic model considering bi-directional relations in a natural disaster: a possible earthquake in Tehran (the Capital of Islamic Republic of Iran). Annals of Operations Research, 269(1-2), 439-473. doi:10.1007/s10479-017-2596-yMills, A. F. (2016). A simple yet effective decision support policy for mass-casualty triage. European Journal of Operational Research, 253(3), 734-745. doi:10.1016/j.ejor.2016.03.005Mills, A. F., Argon, N. T., & Ziya, S. (2013). Resource-Based Patient Prioritization in Mass-Casualty Incidents. Manufacturing & Service Operations Management, 15(3), 361-377. doi:10.1287/msom.1120.0426Mills, A. F., Argon, N. T., & Ziya, S. (2018). Dynamic Distribution of Patients to Medical Facilities in the Aftermath of a Disaster. Operations Research, 66(3), 716-732. doi:10.1287/opre.2017.1695Mollah, A. K., Sadhukhan, S., Das, P., & Anis, M. Z. (2018). A cost optimization model and solutions for shelter allocation and relief distribution in flood scenario. International Journal of Disaster Risk Reduction, 31, 1187-1198. doi:10.1016/j.ijdrr.2017.11.018Na, H. S., & Banerjee, A. (2015). A disaster evacuation network model for transporting multiple priority evacuees. IIE Transactions, 47(11), 1287-1299. doi:10.1080/0740817x.2015.1040929Najafi, M., Eshghi, K., & de Leeuw, S. (2013). A dynamic dispatching and routing model to plan/ re-plan logistics activities in response to an earthquake. OR Spectrum, 36(2), 323-356. doi:10.1007/s00291-012-0317-0Najafi, 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. doi:10.1016/j.tre.2012.09.001Natarajarathinam, 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. doi:10.1108/09600030910996251New Madrid Seismic Zone: Catastrophic earthquake response planning project. (2009). Available atwww.ideals.illinois.edu/bitstream/handle/2142/14810/ImpactofNewMadridSeismicZoneEarthquakeso%20theCentral%20USAVol1.pdf.Ni, W., Shu, J., & Song, M. (2017). Location and Emergency Inventory Pre-Positioning for Disaster Response Operations: Min-Max Robust Model and a Case Study of Yushu Earthquake. Production and Operations Management, 27(1), 160-183. doi:10.1111/poms.12789Niessner, H., Rauner, M. S., & Gutjahr, W. J. (2018). A dynamic simulation–optimization approach for managing mass casualty incidents. Operations Research for Health Care, 17, 82-100. doi:10.1016/j.orhc.2017.07.001Nolz, P. C., Semet, F., & Doerner, K. F. (2011). Risk approaches for delivering disaster relief supplies. OR Spectrum, 33(3), 543-569. doi:10.1007/s00291-011-0258-zOverstreet, R. E., Hall, D., Hanna, J. B., & Kelly Rainer, R. (2011). Research in humanitarian logistics. Journal of Humanitarian Logistics and Supply Chain Management, 1(2), 114-131. doi:10.1108/204267411111584