Recent advances in disaster emergency response planning: Integrating optimization, machine learning, and simulation
), Graphical Abstract
Abstract
The increasing frequency and severity of natural disasters underscore the critical importance of effective disaster emergency response planning to minimize human and economic losses. This survey provides a comprehensive review of recent advancements (2019–2024) in five essential areas of disaster emergency response planning: evacuation, facility location, casualty transport, search and rescue, and relief distribution. Research in these areas is systematically categorized on the basis of methodologies, including optimization models, machine learning, and simulation, with a focus on their individual strengths and synergies. A notable contribution of this work is its examination of the interplay between machine learning, simulation, and optimization frameworks, highlighting how these approaches can address the dynamic, uncertain, and complex nature of disaster scenarios. By identifying key research trends and challenges, this study offers valuable insights for improving the effectiveness and resilience of emergency response strategies in future disaster planning efforts.
References
Alam, M. J., Habib, M. A., Husk, D. 2022. Evacuation planning for persons with mobility needs: A combined optimization and traffic microsimulation modelling approach. International Journal of Disaster Risk Reduction, 80: 103164.
Allison, J., Li, Z., Ma, C., Zhang, Y., Wang, X. B. 2024. Performance evaluation of reinforcement learning-based adaptive signal control deployment in urban network. IFAC-PapersOnLine, 58: 120–125.
Altay, N., Green, W. G. 2006. OR/MS research in disaster operations management. European Journal of Operational Research, 175: 475–493.
Amideo, A. E., Scaparra, M. P., Sforza, A., Sterle, C. 2021. An integrated user-system approach for shelter location and evacuation routing. Networks, 78: 46–68.
Avishan, F., Elyasi, M., Yanıkoğlu, İ., Ekici, A., Özener, O. Ö. 2023. Humanitarian relief distribution problem: An adjustable robust optimization approach. Transportation Science, 57: 1096–1114.
Bagloee, S. A., Johansson, K. H., Asadi, M. 2019. A hybrid machine-learning and optimization method for contraflow design in post-disaster cases and traffic management scenarios. Expert Systems with Applications, 124: 67–81.
Balcik, B., Yanıkoğlu, İ. 2020. A robust optimization approach for humanitarian needs assessment planning under travel time uncertainty. European Journal of Operational Research, 282: 40–57.
Bayram, V., Yaman, H. 2024. A joint demand and supply management approach to large scale urban evacuation planning: Evacuate or shelter-in-place, staging and dynamic resource allocation. European Journal of Operational Research, 313: 171–191.
Bhattarai, S., Song, Y. 2025. Multistage stochastic programming for integrated network optimization in hurricane relief logistics and evacuation planning. Networks, 85: 3–37.
Bravo, R. Z. B., Leiras, A., Oliveira, F. L. C. 2019. The use of UAVs in humanitarian relief: An application of POMDP-based methodology for finding victims. Production and Operations Management, 28: 421–440.
Caunhye, A. M., Nie, X., Pokharel, S. 2012. Optimization models in emergency logistics: A literature review. Socio-Economic Planning Sciences, 46: 4–13.
Chang, K. H., Chen, T. L., Yang, F. H., Chang, T. Y. 2023. Simulation optimization for stochastic casualty collection point location and resource allocation problem in a mass casualty incident. European Journal of Operational Research, 309: 1237–1262.
Chen, S., Sun, Y., Zhao, T., Jia, M., Tang, T. 2024. Data-driven modeling of hurricane evacuee’s individual decision-making for enhanced hurricane evacuation planning: Florida case study in the COVID-19 pandemic. Natural Hazards Review, 25: 04024032.
Chou, C. C., Chiang, W. C., Chen, A. Y. 2022. Emergency medical response in mass casualty incidents considering the traffic congestions in proximity on-site and hospital delays. Transportation Research Part E: Logistics and Transportation Review, 158: 102591.
Darvishan, A., Lim, G. J. 2021. Dynamic network flow optimization for real-time evacuation reroute planning under multiple road disruptions. Reliability Engineering & System Safety, 214: 107644.
Desi-Nezhad, Z., Sabouhi, F., Dehghani Sadrabadi, M. H. 2022. An optimization approach for disaster relief network design under uncertainty and disruption with sustainability considerations. RAIRO - Operations Research, 56: 751–768.
DuBois, E., Albert, L. A. 2022. Dispatching policies during prolonged mass casualty incidents. Journal of the Operational Research Society, 73: 2536–2550.
Espejo-Díaz, J. A., Guerrero, W. J. 2021. A multiagent approach to solving the dynamic postdisaster relief distribution problem. Operations Management Research, 14: 177–193.
Fan, J., Chang, X., Mišić, J., Mišić, V. B., Kang, H. 2022. DHL: Deep reinforcement learning-based approach for emergency supply distribution in humanitarian logistics. Peer-to-Peer Networking and Applications, 15: 2376–2389.
Fathianpour, A., Evans, B., Jelodar, M. B., Wilkinson, S. 2023. Tsunami evacuation modelling via micro-simulation model. Progress in Disaster Science, 17: 100277.
Feng, Y., Liu, T., Hu, Z., Wang, D., Cheng, T. C. E., Yin, Y. 2021. Casualty transport scheduling considering survival probability and injury classification. Computers & Industrial Engineering, 161: 107655.
Galindo, G., Batta, R. 2013. Review of recent developments in OR/MS research in disaster operations management. European Journal of Operational Research, 230: 201–211.
Gama, R., Fernandes, H. L. 2021. A reinforcement learning approach to the orienteering problem with time windows. Computers & Operations Research, 133: 105357.
Harris, A., Roebber, P., Morss, R. 2022. An agent-based modeling framework for examining the dynamics of the hurricane-forecast-evacuation system. International Journal of Disaster Risk Reduction, 67: 102669.
Hasan, M. H., Van Hentenryck, P. 2021. Large-scale zone-based evacuation planning, part II: Macroscopic and microscopic evaluations. Networks, 77: 341–358.
Idoudi, H., Ameli, M., Nguyen Van Phu, C., Zargayouna, M., Rachedi, A. 2022. An agent-based dynamic framework for population evacuation management. IEEE Access, 10: 88606–88620.
Jat, M. N., Rafique, R. A. 2020. Mass-casualty distribution for emergency healthcare: A simulation analysis. International Journal of Disaster Risk Science, 11: 364–377.
Jenkins, P. R., Robbins, M. J., Lunday, B. J. 2021a. Approximate dynamic programming for military medical evacuation dispatching policies. INFORMS Journal on Computing, 33: 2–26.
Jenkins, P. R., Robbins, M. J., Lunday, B. J. 2021b. Approximate dynamic programming for the military aeromedical evacuation dispatching, preemption-rerouting, and redeployment problem. European Journal of Operational Research, 290: 132–143.
Jin, Z., Ng, K. K. H., Zhang, C., Liu, W., Zhang, F., Xu, G. 2024. A risk-averse distributionally robust optimisation approach for drone-supported relief facility location problem. Transportation Research Part E: Logistics and Transportation Review, 186: 103538.
Karabuk, S., Manzour, H. 2019. A multi-stage stochastic program for evacuation management under tornado track uncertainty. Transportation Research Part E: Logistics and Transportation Review, 124: 128–151.
Karampotsis, E., Kioskli, K., Tsirimpa, A., Dounias, G., Polydoropoulou, A. 2024. Understanding evacuation behavior for effective disaster preparedness: A hybrid machine learning approach. Natural Hazards, 120: 13627–13665.
Keykhaei, M., Samany, N. N., Jelokhani-Niaraki, M., Zlatanova, S. 2024. Multi-agent-based human cognition simulation of Situation-aware earthquake emergency evacuation. International Journal of Disaster Risk Reduction, 100: 104183.
Khalili-Damghani, K., Tavana, M., Ghasemi, P. 2022. A stochastic bi-objective simulation–optimization model for cascade disaster location-allocation-distribution problems. Annals of Operations Research, 309: 103–141.
Kim, K., Kaviari, F., Pant, P., Yamashita, E. 2022. An agent-based model of short-notice tsunami evacuation in Waikiki, Hawaii. Transportation Research Part D: Transport and Environment, 105: 103239.
Lee, D. H., Ahn, J. 2024. Multi-start team orienteering problem for UAS mission re-planning with data-efficient deep reinforcement learning. Applied Intelligence, 54: 4467–4489.
Lee, H. R., Lee, T. 2021. Multi-agent reinforcement learning algorithm to solve a partially-observable multi-agent problem in disaster response. European Journal of Operational Research, 291: 296–308.
Lee, S., Lee, Y. H. 2020. Improving emergency department efficiency by patient scheduling using deep reinforcement learning. Healthcare, 8: 77.
Li, Y., Zhang, J., Yu, G. 2020. A scenario-based hybrid robust and stochastic approach for joint planning of relief logistics and casualty distribution considering secondary disasters. Transportation Research Part E: Logistics and Transportation Review, 141: 102029.
Li, Z., Yu, H., Chen, X., Zhang, G., Ma, D. 2019. Tsunami-induced traffic evacuation strategy optimization. Transportation Research Part D: Transport and Environment, 77: 535–559.
Lim, G. J., Rungta, M., Davishan, A. 2019. A robust chance constraint programming approach for evacuation planning under uncertain demand distribution. IISE Transactions, 51: 589–604.
Liu, B., Sheu, J. B., Zhao, X., Chen, Y., Zhang, W. 2020. Decision making on post-disaster rescue routing problems from the rescue efficiency perspective. European Journal of Operational Research, 286: 321–335.
Liu, 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.
Moskal, M. D., Batta, R. 2019. Adaptive unmanned aerial vehicle surveillance using a prize-collecting vertex routing model. Military Operations Research, 24: 5–22.
Mostafizi, A., Wang, H., Cox, D., Dong, S. 2019. An agent-based vertical evacuation model for a near-field tsunami: Choice behavior, logical shelter locations, and life safety. International Journal of Disaster Risk Reduction, 34: 467–479.
Moug, K., Jia, H., Shen, S. 2023. A shared-mobility-based framework for evacuation planning and operations under forecast uncertainty. IISE Transactions, 55: 971–984.
Na, H. S., Banerjee, A. 2019. Agent-based discrete-event simulation model for no-notice natural disaster evacuation planning. Computers & Industrial Engineering, 129: 44–55.
Neamatian Monemi, R., Gelareh, S., Nagih, A., Maculan, N., Danach, K. 2021. Multi-period hub location problem with serial demands: A case study of humanitarian aids distribution in Lebanon. Transportation Research Part E: Logistics and Transportation Review, 149: 102201.
Pei, S. S., Zhai, C. H., Wen, W. P., Yu, P., Wang, Z. Q. 2023. A dynamic patients dispatch and treatment model for resilience evaluation of interdependent transportation-healthcare system. Journal of Earthquake Engineering, 27: 4613–4638.
Poikonen, S., Golden, B. 2020. The mothership and drone routing problem. INFORMS Journal on Computing, 32: 249–262.
Rahman, R., Hasan, S. 2023. A deep learning approach for network-wide dynamic traffic prediction during hurricane evacuation. Transportation Research Part C: Emerging Technologies, 152: 104126.
Rambha, T., Nozick, L. K., Davidson, R., Yi, W., Yang, K. 2021. A stochastic optimization model for staged hospital evacuation during hurricanes. Transportation Research Part E: Logistics and Transportation Review, 151: 102321.
Rauchecker, G., Schryen, G. 2019. An exact branch-and-price algorithm for scheduling rescue units during disaster response. European Journal of Operational Research, 272: 352–363.
Robbins, M. J., Jenkins, P. R., Bastian, N. D., Lunday, B. J. 2020. Approximate dynamic programming for the aeromedical evacuation dispatching problem: Value function approximation utilizing multiple level aggregation. Omega, 91: 102020.
Rodriguez, C. A., Jenkins, P. R., Robbins, M. J. 2023. Solving the joint military medical evacuation problem via a random forest approximate dynamic programming approach. Expert Systems with Applications, 221: 119751.
Sakiani, R., Seifi, A., Khorshiddoust, R. R. 2020. Inventory routing and dynamic redistribution of relief goods in post-disaster operations. Computers & Industrial Engineering, 140: 106219.
Sanci, E., Daskin, M. S. 2021. An integer L-shaped algorithm for the integrated location and network restoration problem in disaster relief. Transportation Research Part B: Methodological, 145: 152–184.
Seraji, H., Tavakkoli-Moghaddam, R., Asian, S., Kaur, H. 2022. An integrative location-allocation model for humanitarian logistics with distributive injustice and dissatisfaction under uncertainty. Annals of Operations Research, 319: 211–257.
Shin, K., Lee, T. 2020. Emergency medical service resource allocation in a mass casualty incident by integrating patient prioritization and hospital selection problems. IISE Transactions, 52: 1141–1155.
Shiri, D., Akbari, V., Hassanzadeh, A. 2024a. The capacitated team orienteering problem: An online optimization framework with predictions of unknown accuracy. Transportation Research Part B: Methodological, 185: 102984.
Shiri, D., Akbari, V., Salman, F. S. 2020. Online routing and scheduling of search-and-rescue teams. OR Spectrum, 42: 755–784.
Shiri, D., Akbari, V., Salman, F. S. 2024b. Online algorithms for ambulance routing in disaster response with time-varying victim conditions. OR Spectrum, 46: 785–819.
Siddig, M., Song, Y. 2025. Multistage stochastic programming with a random number of stages: Applications in hurricane disaster relief logistics planning. European Journal of Operational Research, 321: 925–941.
Sreejith, R., Sinimole, K. R. 2022. Modelling evacuation preparation time prior to floods: A machine learning approach. Sustainable Cities and Society, 87: 104257.
Sun, H., Li, J., Wang, T., Xue, Y. 2022. A novel scenario-based robust bi-objective optimization model for humanitarian logistics network under risk of disruptions. Transportation Research Part E: Logistics and Transportation Review, 157: 102578.
Sun, Y., Huang, S. K., Zhao, X. 2024. Predicting hurricane evacuation decisions with interpretable machine learning methods. International Journal of Disaster Risk Science, 15: 134–148.
Takabatake, T., Nistor, I., St-Germain, P. 2020. Tsunami evacuation simulation for the district of tofino, Vancouver Island, Canada. International Journal of Disaster Risk Reduction, 48: 101573.
Van Steenbergen, R., Mes, M., van Heeswijk, W. 2023. Reinforcement learning for humanitarian relief distribution with trucks and UAVs under travel time uncertainty. Transportation Research Part C: Emerging Technologies, 157: 104401.
Wan, P., Xu, G., Chen, J., Zhou, Y. 2024. Deep reinforcement learning enabled multi-UAV scheduling for disaster data collection with time-varying value. IEEE Transactions on Intelligent Transportation Systems, 25: 6691–6702.
Wang, B., Deng, W., Tan, Z., Zhang, B., Chen, F. 2024a. A two-stage stochastic optimization for disaster rescue resource distribution considering multiple disasters. Engineering Optimization, 56: 1–17.
Wang, R., Liu, W., Li, K., Zhang, T., Wang, L., Xu, X. 2024b. Solving orienteering problems by hybridizing evolutionary algorithm and deep reinforcement learning. IEEE Transactions on Artificial Intelligence, 5: 5493–5508.
Wang, Y., Chen, X., Wang, L. 2023. Deep reinforcement learning-based rescue resource distribution scheduling of storm surge inundation emergency logistics. IEEE Transactions on Industrial Informatics, 19: 10004–10013.
Wilcox, C., Jacobs, P. 2024. Hurricane-battered researchers assess damage. Science, 386: 367.
Xia, H., Sun, Z., Wang, Y., Zhang, J. Z., Kamal, M. M., Jasimuddin, S. M., Islam, N. 2025. Emergency medical supplies scheduling during public health emergencies: Algorithm design based on AI techniques. International Journal of Production Research, 63: 628–650.
Yin, J., Pu, F., Yang, L., D’Ariano, A., Wang, Z. 2023. Integrated optimization of rolling stock allocation and train timetables for urban rail transit networks: A benders decomposition approach. Transportation Research Part B: Methodological, 176: 102815.
Yin, Y., Xu, X., Wang, D., Yu, Y., Cheng, T. C. E. 2024. Two-stage recoverable robust optimization for an integrated location–allocation and evacuation planning problem. Transportation Research Part B: Methodological, 182: 102906.
Yoon, S., Albert, L. A. 2021. Dynamic dispatch policies for emergency response with multiple types of vehicles. Transportation Research Part E: Logistics and Transportation Review, 152: 102405.
Yu, L., Zhang, C., Jiang, J., Yang, H., Shang, H. 2021. Reinforcement learning approach for resource allocation in humanitarian logistics. Expert Systems with Applications, 173: 114663.
Yu, Q., Adulyasak, Y., Rousseau, L. M., Zhu, N., Ma, S. 2022a. Team orienteering with time-varying profit. INFORMS Journal on Computing, 34: 262–280.
Yu, Q., Cheng, C., Zhu, N. 2022b. Robust team orienteering problem with decreasing profits. INFORMS Journal on Computing, 34: 3215–3233.
Zhang, G., Zhu, N., Ma, S., Xia, J. 2021. Humanitarian relief network assessment using collaborative truck-and-drone system. Transportation Research Part E: Logistics and Transportation Review, 152: 102417.
Zhang, M., Zhang, Y., Jiao, Z., Wang, J. 2023. Improving relief operations via optimizing shelter location with uncertain covariates. Transportation Research Part E: Logistics and Transportation Review, 176: 103181.
Zhao, X., Ji, K., Xu, P., Qian, W. W., Ren, G., Shan, X. N. 2020. A round-trip bus evacuation model with scheduling and routing planning. Transportation Research Part A: Policy and Practice, 137: 285–300.
Zheng, Y. J., Du, Y. C., Sheng, W. G., Ling, H. F. 2019. Collaborative human–UAV search and rescue for missing tourists in nature reserves. INFORMS Journal on Applied Analytics, 49: 371–383.
Zhong, S., Cheng, R., Jiang, Y., Wang, Z., Larsen, A., Nielsen, O. A. 2020. Risk-averse optimization of disaster relief facility location and vehicle routing under stochastic demand. Transportation Research Part E: Logistics and Transportation Review, 141: 102015.
Zhou, Y., Li, Z., Meng, Y., Li, Z., Zhong, M. 2021. Analyzing spatio-temporal impacts of extreme rainfall events on metro ridership characteristics. Physica A: Statistical Mechanics and Its Applications, 577: 126053.
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