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<p>Emergency management requires efficient evacuation planning and the delivery of rescue supplies within dynamic road networks disrupted by ongoing disasters. Two critical challenges arise: (1) determining appropriate origin-destination (OD) assignments; and (2) identifying optimal paths among multiple OD pairs in real time. However, traditional static path optimization (SPO) and dynamic path optimization (DPO) often fall short in adapting to rapidly evolving conditions, risking failure in emergency response. To address these limitations, we proposed a novel method by modifying the co-evolutionary path optimization (CEPO) based on the ripple spreading algorithm (RSA), which can simultaneously determine optimal OD pairs and corresponding paths in a single run, even under dynamic disaster environment. The effectiveness and advantages of the method are verified by comprehensive experiments.</p>

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Responsibility Area Redivision and Path Optimization for Emergency Management in Dynamic Disaster Environments

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月27日

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  • Zihao Sang,
  • Yingfei Zhang,
  • Xiangzhi Meng,
  • Hang Li,
  • Xiaobing Hu

摘要

Emergency management requires efficient evacuation planning and the delivery of rescue supplies within dynamic road networks disrupted by ongoing disasters. Two critical challenges arise: (1) determining appropriate origin-destination (OD) assignments; and (2) identifying optimal paths among multiple OD pairs in real time. However, traditional static path optimization (SPO) and dynamic path optimization (DPO) often fall short in adapting to rapidly evolving conditions, risking failure in emergency response. To address these limitations, we proposed a novel method by modifying the co-evolutionary path optimization (CEPO) based on the ripple spreading algorithm (RSA), which can simultaneously determine optimal OD pairs and corresponding paths in a single run, even under dynamic disaster environment. The effectiveness and advantages of the method are verified by comprehensive experiments.