@article{Pu2025, 
author = {Fan Pu and Zihao Li and Yifan Wu and Chaolun Ma and Ruonan Zhao},
title = {Recent advances in disaster emergency response planning: Integrating optimization, machine learning, and simulation},
year = {2025},
journal = {Safety Emergency Science},
volume = {1},
number = {1},
pages = {9590007},
keywords = {machine learning, simulation, optimization model, disaster emergency response planning},
url = {https://www.sciopen.com/article/10.26599/SES.2025.9590007},
doi = {10.26599/SES.2025.9590007},
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.}
}