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<p>EV–UAV collaborative routing provides a practical option for post-disaster emergency logistics, particularly when ground infrastructure is severely disrupted. However, optimizing such multi-modal systems is mathematically challenging due to the strict coupling of vehicle routing sequences, temporal synchronization, and charging feasibility and energy constraints under high-altitude post-disaster conditions. Existing metaheuristic algorithms often struggle with the high-dimensional and discrete nature of this problem, which may lead to premature convergence and entrapment in local optima. To address this issue, this study proposes a Genetic-Operator-Integrated Grey Wolf Optimizer (GOI-GWO). The proposed method employs a discrete encoding strategy to adapt the continuous search mechanism of GWO to combinatorial routing problems, incorporates genetic operators to improve population diversity and solution quality, and introduces a multi-neighborhood local search procedure to strengthen local refinement. Experiments on benchmark instances show that GOI-GWO outperforms multiple benchmark algorithms in overall solution quality and robustness, especially on medium- and large-scale instances. In the Tingri earthquake case study, GOI-GWO reduces the mean operational cost by 8.4% compared with ALNS and by 5.7% compared with TSGWO, while maintaining a 100% feasibility rate. In addition, the sensitivity analysis indicates that the baseline objective setting provides a reasonable balance between operational cost and service punctuality. These findings indicate that GOI-GWO is an effective and robust approach for post-disaster EV–UAV collaborative emergency logistics.</p>

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A Genetic-Operator-Integrated Grey Wolf Optimizer for Solving the EV-UAV Collaborative Emergency Logistics Problem

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月21日

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  • Can Guo,
  • Ying Qiao,
  • Yuelin Gao,
  • Ruiqian Miao,
  • Wenyu Zhang

摘要

EV–UAV collaborative routing provides a practical option for post-disaster emergency logistics, particularly when ground infrastructure is severely disrupted. However, optimizing such multi-modal systems is mathematically challenging due to the strict coupling of vehicle routing sequences, temporal synchronization, and charging feasibility and energy constraints under high-altitude post-disaster conditions. Existing metaheuristic algorithms often struggle with the high-dimensional and discrete nature of this problem, which may lead to premature convergence and entrapment in local optima. To address this issue, this study proposes a Genetic-Operator-Integrated Grey Wolf Optimizer (GOI-GWO). The proposed method employs a discrete encoding strategy to adapt the continuous search mechanism of GWO to combinatorial routing problems, incorporates genetic operators to improve population diversity and solution quality, and introduces a multi-neighborhood local search procedure to strengthen local refinement. Experiments on benchmark instances show that GOI-GWO outperforms multiple benchmark algorithms in overall solution quality and robustness, especially on medium- and large-scale instances. In the Tingri earthquake case study, GOI-GWO reduces the mean operational cost by 8.4% compared with ALNS and by 5.7% compared with TSGWO, while maintaining a 100% feasibility rate. In addition, the sensitivity analysis indicates that the baseline objective setting provides a reasonable balance between operational cost and service punctuality. These findings indicate that GOI-GWO is an effective and robust approach for post-disaster EV–UAV collaborative emergency logistics.