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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2505.03979 |
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| _version_ | 1866908352320110592 |
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| author | Pu, Fan Li, Zihao Wu, Yifan Ma, Chaolun Zhao, Ruonan |
| author_facet | Pu, Fan Li, Zihao Wu, Yifan Ma, Chaolun Zhao, Ruonan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_03979 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Recent Advances in Disaster Emergency Response Planning: Integrating Optimization, Machine Learning, and Simulation Pu, Fan Li, Zihao Wu, Yifan Ma, Chaolun Zhao, Ruonan Optimization and Control 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. |
| title | Recent Advances in Disaster Emergency Response Planning: Integrating Optimization, Machine Learning, and Simulation |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2505.03979 |