Resilience assessment and recovery of airport departure flights under severe weather
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摘要:
为保障恶劣天气下机场整体性能，科学评估机场离场航班运行韧性，提高航班恢复能力，从而有效缓解天气的影响。给出机场离场航班运行定义，从机场离场航班运行系统性能出发，分析航班离场延误时间、离场总延误时间、离场航班正常率和机场离场航班运行系统综合韧性指数4个指标，对系统在恶劣天气条件下的韧性变化进行评估；提出机场离场航班运行系统性能恢复策略，利用遗传算法对离场延误航班进行优化排序；以2012年北京首都国际机场“721”特大暴雨事件为实例进行数据分析，得到暴雨影响下首都机场的性能指标和韧性指数，对比分析机场离场航班运行系统性能及韧性水平变化。研究结果表明：受暴雨影响，机场离场航班运行系统综合韧性指数由0.4573下降到0.0628，暴雨减小后上升到0.2223；在进行航班优化排序后，离场航班总延误时间减少了24.85%，优化后机场性能恢复速度提升了13.89%，优化后的机场离场航班运行系统最小韧性指数提升了13.38%，系统性能优先恢复至初始状态，表明所提恢复策略有效。
Abstract:In order to ensure the overall performance of the airport under severe weather, scientifically evaluate the resilience of the airport's flight operations, improve flight recovery capabilities, and alleviate the impact of severe weather effectively. This article first gives the definition of airport departure flight operation. Starting from the performance of the airport departure flight operation system, it analyzes flight departure delay time, total departure delay time, departure flight normality rate and airport departure flight operation system comprehensive resilience index four indicators to evaluate the resilience changes of the system under severe weather conditions.It is important to present airport departure flight operating system's performance recovery method, to employ a genetic algorithm to optimize the order of the delayed departure flights. Finally, this article takes the “721” heavy rain event in Beijing Capital International Airport in 2012 as an example to analyze the data, obtains the performance index and resilience index of the Capital Airport under the influence of heavy rain, and compare and analyze the changes in airport departure flight operating system performance and resilience level. The results indicate that under the influence of heavy rain, the comprehensive resilience index of the airport departure flight operation system decreased from 0.4573 to 0.0628, and increased to 0.2223 after the rainstorm decreased. The delay time is reduced by 24.85%, the airport performance recovery speed is increased by 13.89% after optimization, and the minimum resilience index of the optimized airport departure flight operation system is increased by 13.38%, the system performance is given priority to restore to the initial state, indicating the effectiveness of the proposed recovery strategy.
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图 1 时刻$t$计划累计航班量与实际累计航班量对比
Figure 1. Comparison of planned cumulative flight volume and actual cumulative flight volume at time t
图 2 外界干扰下系统韧性变化图
Figure 2. Change diagram of system resilience under external disturbances
图 6 小时计划离场航班与实际离场航班对比
Figure 6. Hourly scheduled departure flight and actual departure flight comparison
图 7 计划累计离场航班与实际累计离场航班对比
Figure 7. Comparison of planned cumulative departure flights and actual cumulative departure flights
图 8 21日19:00至22日03:00顺义区小时降雨量
Figure 8. Hourly rainfall in Shunyi District from 19:00 on the 21st to 03:00 on the 22nd
图 9 首都机场离场航班运行系统性能变化
Figure 9. Performance change of flight operation system of Capital Airport Departure
表 1 不同机型延误成本及航班优先级
Table 1. Delay cost and flight priority of different aircraft types
机型 最大起飞质量/kg 延误成本/(元·h−1) 优先级 轻型机（L） <7000 208 0.3 中型机（M） 7000～136000 2916 4 重型机（H） >136000 4167 5.7 
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表 3 时间分段表
Table 3. Time interval schedule
降雨量 时间段 大雨 [20:00,21:00]，[00:55,02:05] 中雨 [21:05,23:20]，[00:40,00:50]，[02:10,02:30] 小雨 [23:25,00:35]，[02:35,12:00]
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表 4 优化前航班时刻表（部分）
Table 4. Flight schedule before optimization (partial)
航班编号 计划起飞时刻 实际起飞时刻 延误时间/min 航班编号 计划起飞时刻 实际起飞时刻 延误时间/min 1 10:50 21:05 615 11 13:25 01:55 750 2 11:45 21:20 575 12 13:25 03:31 846 3 11:55 22:58 663 13 14:00 21:54 474 4 12:05 21:43 578 14 14:05 22:00 475 5 12:30 22:01 571 15 14:05 02:57 772 6 12:50 23:13 623 16 14:15 23:05 530 7 13:00 21:46 526 17 14:45 22:47 482 8 13:00 01:49 769 18 14:50 21:17 387 9 13:15 21:14 479 19 14:55 21:19 384 10 13:20 22:30 550 20 14:55 22:51 476
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表 5 优化后离场航班时刻表（部分）
Table 5. Optimized departure flight schedule (partial)
航班编号 计划起飞时刻 实际起飞时刻 延误时间/min 航班编号 计划起飞时刻 实际起飞时刻 延误时间/min 1 10:50 21:50 660 11 13:25 22:45 560 2 11:45 21:35 590 12 13:25 03:00 815 3 11:55 22:00 605 13 14:00 22:30 510 4 12:05 21:55 590 14 14:05 22:50 525 5 12:30 22:05 575 15 14:05 22:35 510 6 12:50 22:10 560 16 14:15 23:00 525 7 13:00 22:25 565 17 14:45 23:10 505 8 13:00 22:20 560 18 14:50 23:05 495 9 13:15 22:15 540 19 14:55 23:20 505 10 13:20 22:40 560 20 14:55 03:10 735
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