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<p>The primary challenge in earthquake emergency response is the effective dispatch of large-scale rescue teams to disaster areas after an earthquake disaster occurs, especially as this often involves time constraints and road network interruptions. To solve this, we propose a minimax difference submatrix (MDS) algorithm combined with k-means clustering. First, the k-means method is utilized to cluster the rescue locations with similar features, thereby transforming the non-standard assignment problem (rescue team <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\ne\)</EquationSource> </InlineEquation> rescue location) into a solvable linear assignment problem (LAP). After obtaining the cost matrix, the principle of minimax difference is established; that is, the columns with the largest differences between the maximum and minimum values are selected in sequence for priority assignment. By comparing the results of numerical experiments, it can be seen that the MDS method reduces the computational load effectively compared with the Hungarian algorithm, and can obtain a high-quality approximate optimal solution for the linear assignment problem. Finally, through the case study of simulating the IX-X earthquake intensity scenario in City T, it is demonstrated that the MDS method is an efficient and stable approach for solving the problem of large-scale real-time rescue in earthquake emergencies.</p>

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Application of a new assignment algorithm based on the minimax difference in earthquake emergency rescue

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月14日

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  • Sining Huang,
  • Ying Zhang,
  • Tiantian Qiao,
  • Junwu Dai,
  • Jinlong Liu,
  • Yongqiang Yang

摘要

The primary challenge in earthquake emergency response is the effective dispatch of large-scale rescue teams to disaster areas after an earthquake disaster occurs, especially as this often involves time constraints and road network interruptions. To solve this, we propose a minimax difference submatrix (MDS) algorithm combined with k-means clustering. First, the k-means method is utilized to cluster the rescue locations with similar features, thereby transforming the non-standard assignment problem (rescue team \(\ne\) rescue location) into a solvable linear assignment problem (LAP). After obtaining the cost matrix, the principle of minimax difference is established; that is, the columns with the largest differences between the maximum and minimum values are selected in sequence for priority assignment. By comparing the results of numerical experiments, it can be seen that the MDS method reduces the computational load effectively compared with the Hungarian algorithm, and can obtain a high-quality approximate optimal solution for the linear assignment problem. Finally, through the case study of simulating the IX-X earthquake intensity scenario in City T, it is demonstrated that the MDS method is an efficient and stable approach for solving the problem of large-scale real-time rescue in earthquake emergencies.