A Method Based on Point Fusion Procedure for Scheduling Arrival Flights on Multiple Runways
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摘要: 为提高点融合程序下的多跑道机场的进场航班运行效率, 考虑点融合程序下终端区进场程序结构复杂的特点, 提出以0-1整数规划为基础的多跑道进场航班优化排序模型。以进场航班的总延误时间、总飞行时间为最小目标函数, 以尾流间隔、跑道限制、进场航班的飞行时间范围以及可分配进场程序为约束条件, 将不同进场程序及跑道分配给不同的进场航班, 确定航班的飞行时间、落地时刻, 最终求得航班的落地序列。以浦东机场进场程序为例, 选取含精英策略的非支配遗传算法对浦东机场的双落跑道进行进场航班优化排序, 最后与实际结果对比。优化方案的飞行时间和延误时间分别为51 048 s和1 174 s, 相较于实际结果降低了2.1%和38.2%, 单位小时内跑道着陆架次提高了7架, 跑道流量提高了20%左右。Abstract: An optimization ranking model for multi-runway approach flight based on 0-1 integer programming is proposed considering the complex structure of terminal area arrival program under point merge system to improve the operation of approach flights at multi-runway airports based on a point fusion system. Different approach procedures and runways are assigned to different approach flights to determine the flight time and landing time of flights with the total delay time and total flight time of the approach flights as the minimum objective function, and the wake interval, runway limit, flight time range of the approach flights and assignable approach procedure as constraints. The landing sequence of flights is obtained. Taking the approach procedure of Pudong Airport as a case study, the non-dominated genetic algorithm containing elite strategy is selected to optimize the sequencing of approach flights for the double landing runway of Pudong Airport. Then compared the results with the actual results. The flight time and delay time of the optimized program is 51 048 and 1 174 s, respectively, which are 2.1% and 38.2% lower compared with the actual results, and the number of runway landing sorties per unit hour is increased by 7 while the runway flow is increased by about 20%.
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表 1 各程序进场飞行时间
Table 1. Approach flight time of each procedure
单位: s 进场程序编号 飞行时间最小值 飞行时间下四分位数 飞行时间中位数 飞行时间上四分位数 飞行时间最大值 SASAN_81A 1 200 1 301 1 338 1 377 1 713 ASAN_82A 1 211 1 301 1 326 1 355 1 675 BK_81A 1 193 1 235 1 267 1 328 1 413 BK_82A 1 166 1 231 1 274 1 320 1 445 BK_83A 1 152 1 236 1 275 1 321 1 444 DUMET_81A 1 490 1 720 1 734 1 752 1 834 DUMET_82A 1 459 1 719 1 731 1 738 1 813 DUMET_83A 1 291 1 305 1 312 1 360 1 402 MATNU_81A 1 205 1 214 1 215 1 241 1 397 MATNU_82A 1 205 1 214 1 216 1 251 1 497 表 2 优化排序结果
Table 2. Optimized sorting results
到达跑道 优化进场航路 进场时间/s 优化排序结果 优化到达时间/s 优化飞行时间/s 预计到达时间/s 实际到达时间/s 1 11 48 908 1 50 213 1 305 50 202 50 640 1 1 49 137 3 50 407 1 231 50 496 50 516 2 7 51 573 2 50 438 1 301 50 393 50 430 1 7 49 176 4 50 517 1 314 50 459 50 497 2 1 51 846 5 50 548 1 309 50 559 50 966 1 7 49 203 6 50 784 1 305 50 735 50 787 1 11 49 239 7 50 988 1 301 50 981 51 025 2 7 51 876 8 51 004 1 231 51 029 51 068 1 11 49 479 9 51 098 1 322 51 070 51 090 2 7 51 903 11 51 146 1 305 51 097 51 560 1 1 49 687 12 51 324 1 301 51 343 51 331 2 1 52 087 13 51 324 1 301 51 343 51 331 1 7 49 773 14 51 434 1 334 51 356 51 474 1 6 49 776 10 51 544 1 736 51 057 51 272 1 10 49 808 15 51 654 1 317 52 050 51 638 1 11 49 841 16 51 764 1 325 51 733 52 169 1 7 50 023 17 51 882 1 231 51 907 51 888 1 1 50 023 18 52 211 1 301 52 230 52 264 2 12 52 113 19 52 224 1 301 52 120 52 368 1 11 50 100 21 52 351 1 305 52 340 52 786 2 12 52 120 22 52 503 1 257 52 443 52 583 1 1 50 337 23 52 547 1 275 52 592 52 485 1 11 50 439 20 52 699 1 720 52 273 52 287 2 7 52 172 24 52 706 1 214 52 748 52 704 1 7 50 651 25 52 809 1 258 52 807 52 870 2 11 52 239 26 52 816 1 243 52 867 52 981 1 1 50 910 28 53 107 1 231 53 196 53 106 2 12 52 413 27 53 147 1 301 53 043 53 143 1 1 50 923 29 53 217 1 314 53 223 53 186 1 9 50 979 31 53 327 1 214 53 829 53 331 2 7 52 476 32 53 334 1 214 53 376 53 417 1 11 51 046 30 53 437 1 350 53 381 53 489 2 1 52 495 33 53 444 1 272 53 492 53 405 1 2 51 246 34 53 547 1 308 53 547 53 999 2 13 52 713 35 53 627 1 214 53 611 53 627 1 7 51 272 36 53 707 1 231 53 732 53 712 1 12 51 492 37 53 817 1 322 53 692 53 766 1 8 51 551 38 53 927 1 214 53 970 53 927 -
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