综合优化机场表面无人跟踪车的时间安排
Dezhou Yuan1, Xinping Zhu2, Yajun Zou1
1College of Air Traffic Management, Civil Aviation Flight University of China, Deyang, 618307, China.
Scientific reports
|April 12, 2024
概括
本研究介绍了机场无人跟踪汽车的优化调度方法,提高了效率和安全. 新方法平衡了资源分配,运营时间和飞机起飞顺序.
科学领域:
- 运营研究 运营研究
- 运输科学 运输科学
- 人工智能的人工智能
背景情况:
- 大型机场在有效管理地面运营方面面临挑战.
- 自动驾驶汽车,特别是无人跟踪汽车,为机场物流提供了潜在的解决方案.
- 当前的调度方法可能无法充分解决机场运营和安全的复杂性.
研究的目的:
- 开发一个集成的优化方法来安排无人跟踪我汽车.
- 通过优化车辆调度,提高机场运营的效率和安全性.
- 为机场环境中的车辆分配,指导和回收提供定量框架.
主要方法:
- 一个分为三个阶段的调度过程:调度,指导和回收.
- 开发一个用于公平分配资源的车辆分配模型.
- 建立一个协作规划模型和一个冲突预测模型.
- 利用改进的灰狼优化算法来提高性能.
主要成果:
- 协作规划模型有效地平衡了路线选择,车辆工作时间和出发顺序.
- 改进的灰狼优化算法显示,汇速度增加了18.75%.
- 车辆分配不公平指数减少到0.015731.
- 机场表面布局被确定为影响冲突时空分布的关键因素.
结论:
- 综合优化方法显著改善了机场无人跟踪车的调度.
- 开发的模型提高了运营效率,资源配置和安全.
- 改进的算法为机场物流优化提供了卓越的融合速度和概括性能.
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