Arrival flights optimal sequencing with multi-path selection based on rolling horizon control
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摘要:
进场航班排序优化是提高进场航班着落效率、减少航班延误的有效方法。基于此，以最大化着落效率为目标，结合多跑道、多航路选择，考虑实际航路点限制，提出了多路径多跑道一体化进场航班排序优化混合整数规划模型。为解决大规模航班排序计算的实时性问题，提出了多航路点滚动时域控制算法。以广州白云国际机场终端区为实例进行验证，采用实际进场航班数据开展计算实验，在尾流安全间隔上，采用RECAT-CN运行标准，计算结果表明：小规模航班架次时（23架），所提模型最大降落时间比先到先服务方法提前55 s，比未优化时提前271 s；大规模航班架次时（104架），仅靠求解器在3600 s内未找到可行解，所提算法在128.65 s找到解。所提模型和算法有效，可应用于实际航班排序优化。
Abstract:Arrival flight sequencing is an effective strategy to improve landing efficiency and reduce flight delays. On the basis of previous research, this paper aims to minimize the makespan of all flights landing, considers actual multi-waypoint constraints, and proposes a path and runway integrated model, in which runway and path assignment can be achieved simultaneously based on real constraints. In order to solve the model under large-scale conditions in real-time, a multi-way point rolling horizon control algorithm is proposed. In the verification part, we take the Guangzhou Baiyun International Airport terminal area as the background and use the actual arrival data to carry out the calculation experiment. In the wake safety interval, we adopt a more detailed RECAT-CN operating standard. Based on the data from the 23 flights, the computational findings demonstrate that the presented methodology reduces wait times by 271 seconds and 55 seconds when compared to first-come, first-served and natural sequencing, respectively. Using the data of 104 flights to test the solving ability, the computational results show the solving time is 128.65 seconds with the proposed algorithm while no feasible solution is found within 3600 seconds by solver. It will state the proposed model and algorithm works well and could be used in real sequencing.
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图 2 MWRHC算法第1时域和第2时域示意图
Figure 2. Schematic diagram of the first and the second horizons of MWRHC algorithm
图 4 进场航班到走廊口预计到达时间分布
Figure 4. Distribution of estimate arrival time of arrival flights to corridor gate
前机 后机 J B C M L J MRS 9.3 11.1 13.0 14.8 B MRS 5.6 7.4 9.3 13.0 C MRS MRS MRS 6.5 11.1 M MRS MRS MRS MRS 9.3 L MRS MRS MRS MRS MRS 
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表 2 进近航路飞行路线
Table 2. Approach routes of flight path
走廊口 航路编号 进场航路 GYA 1 GYA—AGVOS—GG404—GG401—01 2 GYA—AGVOS—GG404—GG407—GG403—02R IGONO 3 IGONO—GG442—TAN—AGVOS—GG404—GG401—01 4 IGONO—GG442—TAN—AGVOS—GG404—GG407—GG403—02R 5 IGONO—GG442—CON—CEN—GG408—GG407—GG401—01 6 IGONO—GG442—CON—CEN—GG408—GG407—GG403—02R ATAGA 7 ATAGA—GG428—TAN—AGVOS—GG404—GG401—01 8 ATAGA—GG428—TAN—AGVOS—GG404—GG407—GG403—02R 9 ATAGA—GG428—CON—CEN—GG408—GG407—GG401—01 10 ATAGA—GG428—CON—CEN—GG408—GG407—GG403—02R P270 11 P270—SHL—CEN—GG408—GG407—GG401—01 12 P270—SHL—CEN—GG408—GG407—GG403—02R IDUMA 13 IDUMA—SHL—CEN—GG408—GG407—GG401—01 14 IDUMA—SHL—CEN—GG408—GG407—GG403—02R
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表 3 模型求解结果对比
Table 3. Comparison of model solution results
航班序号 机型 到走廊口时间/s 走廊口 航班降落时间/s 优化前 FCFS MILP 1 M 0 GYA 955 955 1420 2 M 40 IGONO 1372 1668 1372 3 M 215 IGONO 1547 1738 1772 4 M 249 IGONO 1655 1786 1 930 5 M 349 ATAGA 1 835 1 871 2 026 6 M 374 GYA 1 943 2055 1497 7 M 404 IGONO 1 991 2125 2096 8 M 514 IDUMA 2099 2173 1 978 9 B 549 ATAGA 2147 2243 2319 10 B 564 IGONO 2207 2309 2385 11 M 708 GYA 2256 2370 2144 12 M 719 ATAGA 2364 2418 2205 13 M 858 GYA 2472 2466 2433 14 B 1033 IDUMA 2520 2536 2253 15 M 1038 IGONO 2629 2645 2740 16 M 1068 GYA 2677 2715 2670 17 M 1218 IGONO 2737 2829 2880 18 B 1448 ATAGA 2934 2899 2976 19 M 1512 GYA 3043 2947 2810 20 B 1597 IGONO 3091 2995 3097 21 M 1602 IGONO 3200 3104 3049 22 B 1647 P270 3308 3152 2928 23 B 1762 IGONO 3416 3200 3145
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表 4 Gurobi求解器与MWRHC算法结果对比
Table 4. Comparison of algorithm results between Gurobi solver and MWRHC
时段/h 航班
数量目标值/s 计算时间/s Gurobi
求解器MWRHC
算法Gurobi
求解器MWRHC
算法0~0.5 23 3145 3145 51.38 17.95 0~1 41 4917 4917 118.25 27.60 0~1.5 60 6736 6736 383.13 52.31 0~2 77 8559 8559 1697.72 74.20 0~2.5 88 10124 10124 3329.26 77.66 0~3 104 11971 3600 128.65
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