Recent Advances in Disaster Emergency Response Planning: Integrating Optimization, Machine Learning, and Simulation

TL;DR

This survey reviews recent advancements (2019-2024) in disaster emergency response planning, focusing on optimization, machine learning, and simulation to improve strategies for evacuation, facility location, casualty transport, search and rescue, and relief distribution.

Contribution

It systematically categorizes research methodologies and examines their interplay, highlighting how combined approaches can better address complex disaster scenarios.

Findings

Integration of machine learning, simulation, and optimization enhances response planning.

Identification of key research trends and challenges in disaster response.

Insights into improving resilience and effectiveness of emergency 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.