Zhang Jie, He Junnan, Ren Shihao, Zhou Pei, Guo Jun, Song Mingyue
College of Energy and Transportation Engineering, Inner Mongolia Agricultural University, Hohhot, 010010, China.
School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan, 430070, China.
Sci Rep. 2025 Jan 11;15(1):1725. doi: 10.1038/s41598-025-85638-3.
In the face of forest fire emergencies, fast and efficient dispatching of rescue vehicles is an important means of mitigating the damage caused by forest fires, and is an effective method of avoiding secondary damage caused by forest fires, minimizing the damage caused by forest fires to the ecosystem, and mitigating the losses caused by economic development. this paper takes the actual problem as the starting point, constructs a reasonable mathematical model of the problem, for the special characteristics of the emergency rescue vehicle scheduling problem of forest fires, taking into account the actual road conditions in the northern pristine forest area, through the analysis of the cost of paths between the forest area and the highway, to obtain the least obstructed rescue paths, to narrow the gap between the theoretical model and the problem of the actual. Improvement of ordinary genetic algorithm, design of double population strategy selection operation, the introduction of chaotic search initialization population, to improve the algorithm's solution efficiency and accuracy, through the northern pristine forest area of Daxing'anling real forest fire cases and generation of large-scale random fire point simulation experimental test to verify the effectiveness of the algorithm, to ensure that the effectiveness and reasonableness of the solution to the problem of forest fire emergency rescue vehicle scheduling program. It enriches the solution method of forest fire emergency rescue vehicle dispatching problem in Great Khingan area, which is of great significance to improve the emergency rescue capability in case of sudden forest fire. Through simulation experiments, the proposed Improved Genetic Algorithm (IGA) achieved an average rescue time reduction of 8.5% compared to conventional Genetic Algorithm (GA) and 3.5% compared to Improved Artificial Bee Colony (IABC) algorithm, with an average solution time of 9.4 ms.
面对森林火灾突发事件，快速高效地调度救援车辆是减轻森林火灾造成损害的重要手段，也是避免森林火灾引发二次损害、将森林火灾对生态系统的损害降至最低以及减轻经济发展损失的有效方法。本文以实际问题为出发点，构建合理的数学模型，针对森林火灾应急救援车辆调度问题的特殊特性，考虑北方原始林区的实际路况，通过分析林区与公路之间路径的成本，获取阻碍最小的救援路径，以缩小理论模型与实际问题之间的差距。改进普通遗传算法，设计双种群策略选择操作，引入混沌搜索初始化种群，提高算法的求解效率和精度，通过大兴安岭北方原始林区真实森林火灾案例及生成大规模随机火点进行模拟实验测试，验证算法的有效性，确保森林火灾应急救援车辆调度方案问题求解的有效性和合理性。它丰富了大兴安岭地区森林火灾应急救援车辆调度问题的求解方法，对提高突发森林火灾情况下的应急救援能力具有重要意义。通过模拟实验，所提出的改进遗传算法（IGA）与传统遗传算法（GA）相比，平均救援时间减少了8.5%，与改进人工蜂群算法（IABC）相比减少了3.5%，平均求解时间为9.4毫秒。
