Recent Advances in Disaster Emergency Response Planning: Integrating Optimization, Machine Learning, and Simulation
Fan Pu, Zihao Li, Yifan Wu, Chaolun Ma, Ruonan Zhao
TL;DR
This paper surveys disaster emergency response planning literature from 2019 to 2024 across evacuation, facility location, casualty transport, search and rescue, and relief distribution. It organizes work by optimization, data-driven methods, and simulation, and analyzes how these paradigms can be integrated to address uncertainty and dynamics in disasters. Key contributions include identifying trends such as robust and risk-aware optimization, data-driven decision support, and ABM/simulation as validation and scenario analysis tools, along with synergies between methods. The findings offer guidance for practitioners to design faster, fairer, and more resilient emergency response strategies.
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 based on 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 to improve the effectiveness and resilience of emergency response strategies in future disaster planning efforts.