公路应急救援车辆智能调度技术

doi:10.6040/j.issn.1672-3961.0.2023.045

摘要/Abstract

摘要：

交通事故具有突发性、复杂性和严重性, 如何高效合理地调度应急救援车辆, 对减少人员伤亡和财产损失起到至关重要的作用。介绍救援路径规划的各个影响因素, 并从单目标模型和多目标模型两个角度对模型进行阐述。结合突发交通事件紧急救援的特点, 从精确算法和元启发式算法两个方面综述应急救援车辆路径优化算法。对当前的应急救援车辆调度技术和发展趋势进行总结和展望。

关键词: 应急救援, 路径优化, 智能调度, 数学建模, 模拟仿真

Abstract:

Traffic accidents had suddenness, complexity and severity. Efficient and reasonable dispatch of emergency rescue vehicles played a vital role in reducing casualties and property losses. The influencing factors of rescue route planning were introduced. The model was described from the single objective model and the multi-objective model. Combined with the characteristics of emergency rescue in sudden traffic accidents, the route optimization algorithm of emergency rescue vehicles was summarized from two aspects: precise algorithm and meta-heuristic algorithm. The current emergency rescue vehicle scheduling technology and development trend were summarized and prospected.

Key words: emergency rescue, path optimization, intelligent scheduling, mathematical modeling, simulation

中图分类号:

U491.1+2

宋修广,郭鑫铭,闫方,李国强,田源. 公路应急救援车辆智能调度技术[J]. 山东大学学报 (工学版), 2023, 53(4): 1-17.

Xiuguang SONG,Xinming GUO,Fang YAN,Guoqiang LI,Yuan TIAN. Intelligent scheduling technology of highway emergency rescue vehicle[J]. Journal of Shandong University(Engineering Science), 2023, 53(4): 1-17.

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模型类别	影响因素	特点	不足
单目标模型	救援距离	应急状态下, 所有车辆行驶路径的总长度最短	多事件非同步发生时, 响应途中的应急救援车辆无法根据实际情况调整
	救援成本	通常考虑每公里的行驶成本和车辆启用数, 尽可能减少运输费用	可能会导致救援时间延误和救援物资提供不足
	响应时间	提高服务效率, 减少车辆行驶时间, 减少突发事件造成的资产和生命损失	缩短响应时间的同时, 也增加了救援过程的风险性, 增大了二次事故发生的概率
多目标模型		综合考虑多个因素的影响, 使模型与实际问题更相符	目标增多会增加决策选择的复杂度

固体退火	模拟退火算法
粒子状态	可行解
粒子能量最低状态	最优解
设置初始温度	初始解
能量	目标函数
冷却	控制参数的下降
等温过程	Metropolis抽样过程

算法	优势	不足
精确算法	对于小规模的车辆优化调度问题具有求解速度快、求解精度高等优点	计算复杂度与问题规模之间呈几何级数变化关系, 不能满足实际应用需求
元启发式算法	适用于求解大规模复杂的实际问题, 处理速度相对精确算法更快	无法保证得到某优化问题的全局最优解; 无法有效地处理约束条件, 可能导致最终解为不可行解

算法	优势	不足
蚁群算法	具有强大的鲁棒性和寻找较优解的能力	收敛速度慢，容易陷入最优解
粒子群算法	全局搜索能力强, 收敛速度快, 简单容易实现, 并且没有许多参数需要调整	容易陷入过早收敛, 局部搜索能力弱
模拟退火算法	能适应于许多组合和连续优化问题	收敛缓慢, 调参问题复杂
遗传算法	具有很强的鲁棒性和优化能力	在进化后期搜索效率较低
布谷鸟算法	可以在全局范围内进行更有效的探索, 提高寻优效率	面对多峰复杂函数时对空间的探索仍具有一定的盲目性

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