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摘要: 为提高终端区运行效率和减小航班延误, 考虑了空域容量和安全间隔等约束, 以最小化航班总燃油消耗、均衡进场点等待时间和最小化航班总延误为优化目标, 建立了终端区空域进离场资源分配优化模型, 设计了带精英策略的非支配排序遗传算法, 使用上海终端区实际运行数据进行实例验证。计算结果表明: 当SASAN进场节点容量下降时, 与先到先服务策略相比, 进场点分配策略下总燃油消耗由462 282.7 kg降为337 752.9 kg, 减少了26.9%, HC、CO、NOx排放量分别由492.6、3 815.7、16 570.6 kg降为429.2、3 352.1、14 129.1 kg, 进场点总等待时间减少了93.5%, 所有航班平均延误降为104 s, 94.6%的航班的延误在600 s以内, 因此, 优化模型能有效解决终端区交通需求不均衡或节点容量下降导致的延误, 提高终端区运行效率。Abstract: In order to improve the operation efficiency of terminal area and reduce the flight delay, some factors such as airspace capacity and safety interval were considered, the minimum total flight fuel consumption, balance arrival fix holding time and minimum total flight delay were taken as optimization objectives, the optimization model of arrival and departure resource allocation in terminal area was established, the elitist non-dominated sorting genetic algorithm was designed, and example verification was carried out by using the real operation data of Shanghai terminal area.Calculation result shows that when the capacity of arrival fix SASAN decreases, compared with the first come first service(FCFS)strategy, the total fuel consumption decreases by 26.9% from 462 282.7 kg to 337 752.9 kg by using arrival fix allocation(AFA)strategy, HC, CO and NOx emissions decrease from 492.6, 3 815.7 and 16 570.6 kg to 429.2, 3 352.1 and 14 129.1 kg respectively, total arrival fix holding time decreases by 93.5%, the average delay time of all flights decreases to 104 s, and the delays of 94.6% of flights are lessthan 600 s.Obviously, the optimization model can effectively solve the delay in terminal area because of traffic demand unbalance and fix capacity decrease, and improve the operation efficiency of terminal area.
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Key words:
- air transportation /
- terminal area /
- resource allocation /
- multi-objective optimization /
- AFA strategy /
- fuel consumption /
- emission /
- flight delay
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图 5 种群进化过程中总燃油消耗量变化趋势
Figure 5. Changing trends of total fuel consumption in population evolution
图 6 不同策略下的燃油消耗量对比
Figure 6. Comparison of fuel consumptions under different strategies
图 8 种群进化过程中航班平均进场点等待时间变化趋势
Figure 8. Changing trends of average arrival fix holding time of flights in population evolution
图 12 种群进化过程中航班总延误变化趋势
Figure 12. Changing trends of total flight delay in population evolution
图 13 不同策略下累积延误架次与延误时间的关系
Figure 13. Relationships between accumulated number of delayed flights and delay time under different strategies
表 1 恶劣天气下进场节点容量的变化
Table 1. Fluctuation of arrival fix capacities under severe weather condition
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表 2 不同策略下不同延误对应的架次占比
Table 2. Flight proportion with different delay times under different strategies
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