面向节油减排的平行多跑道混合运行机场停机位分配模型

王超; 任云鸿

doi:10.3963/j.jssn.1674-4861.2021.05.018

面向节油减排的平行多跑道混合运行机场停机位分配模型

doi: 10.3963/j.jssn.1674-4861.2021.05.018

王超^,,
任云鸿

中国民航大学空中交通管理学院天津 300300

基金项目:

中国民航环境与可持续发展研究中心（智库）开放基金项目 CESCA2019Z02

详细信息

通讯作者:
王超（1971—），博士，教授.研究方向：空中交通系统运行与仿真. E-mail：atmofcauc@163.com

中图分类号: U8
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出版历程
- 收稿日期: 2021-03-12

A Model of Gate Allocation for Parallel Multi-runway Hybrid Operation from the Perspective of Fuel-saving and Carbon Emission Reduction

WANG Chao^,,
REN Yunhong

College of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China

摘要

摘要:
针对因隔离平行运行模式与不合理跑道机位使用方案引起的航空器场面滑行排放过高的停机位分配问题，在传统停机位分配模型基础上，研究了多跑道运行模式对停机位分配方案的影响，基于空管机场2方协同运行与就近起降运行模式，研究了面向平行多跑道混合运行的停机位分配模型。通过引入航空器空中走向约束与航班接续约束，以减少采用航班横跨场面运行这个过长滑行距离的停机位分配方案。在此基础上，考虑不同机型发动机燃油流率对分配方案燃油消耗及碳排放的影响，以最小化燃油消耗为目标建立整数规划数学模型，并结合天津机场典型时段运行数据进行仿真验证。仿真结果表明：与原计划运行结果相比，优化策略的滑行距离与碳排放分别减少了11.9% 和13.3%，说明通过优化多跑道运行机场的停机位跑道使用方案可有效减少滑行距离与油耗，达到节油减排的目的。
- 航空运输 /
- 停机位分配 /
- 碳减排 /
- 滑入-滑出过程 /
- 整数规划模型 /
- 协同运行
Abstract:
The work aims to solve the problems of gate allocation caused by segregated and parallel operation mode and unreasonable runway-stand usage plan, and the high taxiing emissions of aircraft surface. A traditional model of gate allocation is used to study the influence of the multi-runway operation mode on a stand allocation plan. Based on cooperative operation between air traffic control and airport control center, as well as nearby take-off and landing operation mode, the work proposes a gate allocation model oriented to parallel multi-runway mixed operation. Introducing aircraft flight direction constraints and flight continuity constraints can reduce the use of gate allocation schemes—flights run across the airport's surface, which is an excessively long taxiing distance. The study takes the account the impacts of different engine fuel-flow rates on fuel consumption and carbon emissions of the allocation plan, so the an integer programming mathematical model is established to minimize fuel consumption, and the simulation verification is carried out based on the operation data of Tianjin Airport in typical periods. The results show that taxiing distance and carbon emissions of the optimized strategy are reduced by 11.9% and 13.3%, respectively, compared with the originally planned operation results. The taxiing distance and fuel consumption can be reduced by optimizing the use of stands at airports with multiple runways to achieve fuel-saving and emission reduction.
- air transportation /
- gate allocation /
- carbon emission reduction /
- sliding in and out process /
- integer programming model /
- coordinate operation

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图 1 平行双跑道机场地面滑行路径示意图

Figure 1. Schematic diagram of the ground taxi path of the parallel double runway airport

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图 2 平行跑道运行模式分类及间隔要求

Figure 2. Classification and interval requirements of parallel runway operation modes

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图 3 着陆与起飞跑道编号

Figure 3. Landing and take-off runway number

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图 4 航班滑行路径示意图

Figure 4. Schematic diagram of flight taxiing path

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图 5 优化前后滑行距离对比

Figure 5. Comparison of taxi distance before and after optimization

下载: 全尺寸图片幻灯片

表 1 航班运行数据

Table 1. Flight service data

序号	航班号	机型	进场时刻	离场时刻	停机位	进场跑道	进场走向	离场跑道	离场走向
1	GS6658	E190	09：05	10：45	203	34R	东	34L	西
2	3U8567	A320	09：15	10：25	230	34R	东	34L	西
3	9C8813	A320	09：15	10：30	211	34L	西	34L	西
4	CA4187	A321	09：30	10：30	205	34L	西	34L	西
5	EU2775	A320	09：35	10：35	223	34L	西	34L	西
6	MF8175	B738	09：40	10：50	229	34R	西	34L	西
7	AQ1055	B738	09：45	11：05	218	34L	西	34L	西
8	OZ327	A321	09：55	10：55	104	34L	东	34L	东
9	GS7906	A330	09：55	11：20	220	34L	西	34L	西
10	BK2787	B739ER	10：00	13：15	106	34R	西	34L	东
11	MF8125	B738	10：10	13：30	201	34R	西	34L	东
12	GS7581	E190	10：15	11：35	202	34L	西	34L	西
13	GS7958	A332	10：20	17：40	212	34L	西	34L	西
14	FU6743	B738	10：30	11：35	206	34R	西	34L	西
15	FM9147	B737	10：30	11：35	224	34R	西	34L	西
16	CA1678	B738	10：35	11：40	209	34R	东	34L	西
17	SC4801	B738	10：40	11：40	118	34R	西	34L	西
18	MF8209	B738	10：40	11：45	225	34R	东	34L	西
19	ZH9121	B738	10：40	11：45	207	34R	西	34L	西
20	RY8961	B738	10：45	11：45	226	34L	西	34L	西
21	G52866	A320	10：55	12：00	213	34R	西	34L	西
22	3U8810	A321	10：55	15：30	208	34L	西	34L	西

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表 2 慢车状态下发动机燃油流率

Table 2. The fuel flow rate of the engine in slow mode

机型	发动机数量N_i	发动机型号	燃油流率 f_i/(kg/s)
E190	2	CF34-10E	0.085
B737-700	2	CFM56-3C-1	0.124
B737-800	2	CFM56-7B24/2	0.109
B737-900ER	2	CFM56-7B27	0.116
A320	2	CFM56-5A3	0.104 4
A321	2	V2530-A5	0.138
A330	2	CF6-80C2B4	0.199
A332	2	CF6-80E1A2	0.228

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表 3 各停机位与跑道组合对的滑行距离

Table 3. Taxi distance of each stand and runway combination pair m

停机位	进离场选用跑道				停机位	进离场选用跑道
停机位	34L降 34L起	34L降 34R起	34R降 34L起	34R降 34R起	停机位	34L降 34L起	34L降 34R起	34R降 34L起	34R降 34R起
104	357 8	498 7	608 4	749 2	211	433 6	628 1	486 7	681 2
106	369 8	494 2	604 2	728 7	212	425 9	505 6	478 6	558 4
110	440 0	634 8	539 5	734 2	213	469 8	490 0	522 8	543 1
114	472 3	666 7	525 9	720 3	215	450 9	471 1	503 9	524 2
118	511 2	705 6	564 2	758 7	218	516 4	473 9	569 2	526 7
201	505 6	700 1	558 7	753 1	220	640 1	597 5	382 0	339 5
202	500 3	694 8	553 4	747 8	223	702 3	680 6	444 2	422 5
203	497 0	691 4	550 0	744 5	224	700 1	657 6	441 7	399 2
205	480 6	675 1	533 7	728 1	225	698 4	656 2	440 3	398 1
206	474 5	668 9	527 5	722 0	226	698 9	656 4	440 6	398 1
207	468 1	662 6	521 2	715 6	229	709 5	667 0	451 7	409 2
208	464 8	659 2	517 8	712 3	230	710 6	668 1	452 5	410 0
209	459 8	654 2	512 8	707 3

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表 4 滑行距离及燃油对比

Table 4. Comparison of taxiing distance and fuel

航班	初始方案	初始距离/m	初始燃油/kg	优化方案	优化后距离/m	优化后燃油/kg
1	34R-203-34L	5 500	60.6	34R-225-34L	4 403	56.3
2	34R-230-34L	4 525	61.2	34R-220-34L	3 820	67.7
3	34L-211-34L	4 336	58.7	34L-201-34L	5 056	65.0
4	34L-205-34L	4 806	85.9	34L-209-34L	4 598	82.2
5	34L-223-34L	7 023	95.0	34L-212-34L	4 259	67.2
6	34R-229-34L	4 517	63.8	34R-226-34L	4 406	63.9
7	34L-218-34L	5 164	72.9	34L-202-34L	5 003	63.7
8	34R-104-34L	6 084	108.8	34L-106-34R	4 942	88.4
9	34L-220-34L	6 401	165.1	34L-205-34L	4 806	109.8
10	34R-106-34L	6 042	90.8	34L-104-34R	4 987	75.0
11	34R-201-34L	5 587	78.9	34R-229-34R	4 092	56.2
12	34R-202-34L	5 534	61.0	34L-213-34L	4 698	55.7
13	34R-212-34L	4 786	141.4	34L-215-34L	4 509	112.9
14	34R-206-34L	5 275	74.5	34R-224-34L	4 417	62.7
15	34R-224-34L	4 417	71.0	34R-223-34L	4 442	70.8
16	34R-209-34L	5 128	72.4	34L-203-34L	4 970	67.0
17	34R-118-34L	5 642	79.7	34L-206-34L	4 745	66.1
18	34R-225-34L	4 403	62.2	34R-230-34L	4 525	63.8
19	34R-207-34L	5 212	73.6	34L-208-34L	4 648	65.6
20	34R-226-34L	4 406	62.2	34L-118-34L	5 112	62.4
21	34R-213-34L	5 228	70.7	34L-207-34L	4 681	63.6
22	34R-208-34L	5 178	92.6	34L-211-34L	4 336	77.6
合计		115 194	1 803.2		101 453	1 563.6

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表 5 2类模型仿真结果对比表

Table 5. Comparison of simulation results of two types of models

评估项	基于滑行距离的模型	基于燃油消耗的模型
总滑行距离/m	101 452	101 452
平均滑行距离/m	4 611	4 611
总滑行油耗/kg	1 583.2	1 563.6
平均滑行油耗/kg	72.0	71.1

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参考文献(18)

[1]	Air Transport Action Group. Facts & Figures[R/OL]. (2020-09) [2021-03-01]https://www.atag.org/facts-figures.html.
[2]	International Civil Aviation Organization (ICAO)ICAO Environmental Report[R]. Montreal: International Civil Aviation Organization, 2019. (in Chinese)
[3]	FLEUTI E, MARAINI S. Taxi-emissions at Zurich airport[R]. Zurich : Department of Law and Environment, 2017.
[4]	DAS G S, GZARA F, STUTZLE T. A review on airport gate assignment problems: Single versus multi objective approaches[J]. Omega, 2020(92) : 102-146.
[5]	TANG Chinghui, WANG Weichung. Airport gate assignments for airline-specific gates[J]. Journal of Air Transport Manage ment, 2013(30): 10-16. http://www.onacademic.com/detail/journal_1000036037272110_4b2b.html
[6]	KIM S H, FERON E, CLARKE J-P. Gate assignment to minimize passenger transit time and aircraft taxi time[J]. Journal of Guidance, Control, and Dynamic, 2013, 36 (2) : 467-475. doi: 10.2514/1.57022
[7]	DENG Wu, ZHAO Huimin, YANG Xinhua, et al. Study on an improved adaptive PSO algorithm for solving multi-objective gate assignment[J]. Applied Soft Computing, 2017(59)288-302. http://www.sciencedirect.com/science?_ob=ShoppingCartURL&_method=add&_eid=1-s2.0-S1568494617303472&originContentFamily=serial&_origin=article&_ts=1497158556&md5=62bbb4eb20ac416daa60d6b17b638021
[8]	BAGAMANOVA M, MOTA M M. Reducing airport environmental footprint using a disruption-aware stand assignment approach[J] Transportation Research Part D: Transport and Environment, 2020(89) : 102634. http://www.sciencedirect.com/science/article/pii/S1361920920308191
[9]	卫东选. 基于运行安全的机场停机位分配问题研究[D]. 南京: 南京航空航天大学, 2010. WEI Dongxuan. Study on airport Gate assignment problem based-on operational safety[D]. Nanjing : Nanjing University of Aeronautics and Astronautics, 2010. (in Chinese)
[10]	冯稈, 胡明华, 赵征. 一种新的停机位分配优化模型[J]. 交通运输系统工稈与信息, 2012, 12(1): 131-138. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201201021.htm FENG Cheng, HU Minghua, ZHAO Zheng. A new optimization model of airport gate assignment[J]. Journal of Transportation Systems Engineering and Information Technology, 2012, 12(1): 131-138. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201201021.htm
[11]	马思思, 唐小卫. 基于机场滑行效率提升的停机位优化分配模型[J]. 武汉理工大学学报, 2018, 4(4) : 24-30. https://www.cnki.com.cn/Article/CJFDTOTAL-WHGY201804005.htm MA Sisi, TANG Xiaowei. Gate assignment optimization on efficiency improvement of airport taxiing[J]. Journal of Wuhan University of Technology, 2018, 40(4) : 24-30. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-WHGY201804005.htm
[12]	姜雨, 胡志韬, 童楚, 等. 面向航班延误的停机位实时指派优化模型[J]. 交通运输系统工稈与信息, 2020, 20 (5): 185-190+217. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202005027.htm JIANG Yu, HU Zhitao, TONG Chu, et al. An optimization model for gate reassignment under flight delays[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(5): 185-190+217. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202005027.htm
[13]	BENLIC U, BURKE E K, WOODWARD J R. Breakout local search for the multi-objective gate allocation problem[J]. Computers & Operations Research, 2017(78) : 80-93. http://storre.stir.ac.uk/bitstream/1893/24175/1/Benlic_etal_COR_2016.pdf
[14]	YU Chuhang, ZHANG Dong, LAU H Y K. An adaptive large neighborhood search heuristic for solving a robust gate assignment problem[J]. Expert Systems with Applications, 2017 (84): 143-154. http://www.sciencedirect.com/science/article/pii/S0957417417302993
[15]	DELL' ORCO M, MARINELLI M, ALTIERI M G. Solving the gate assignment problem through the fuzzy bee colony optimization[J]. Transportation Research Part C: Emerging Technologies, 2017(80): 424-438. http://www.onacademic.com/detail/journal_1000039873430410_be57.html
[16]	中国民用航空局. 平行跑道同时仪表运行管理规定: CCAR-98TM[S]. 北京: 中国民用航空局, 2004. Civil Aviation Administration of China. Regulations on the management of simultaneous instrument operation on parallel runways: CCAR-98TM[S]. Beijing: Civil Aviation Administration of China, 2004. (in Chinese)
[17]	李冬. 面向节油减排的机场离场过稈排队网络建模与优化[D]. 天津: 中国民航大学, 2020. LI Dong. Modeling and optimization of queuing network in airport departure process for fuel saving and emission reduction[D]. Tianjin: Civil Avition University of China, 2020. (in Chinese)
[18]	International Civil Aviation Organization (ICAO). ICAO engine exhaust emissions databank[DB/OL]. (2019-05)[202103-01].https://www.easa.europa.eu/domains/environment/icaoaircraft-engine-emissions-databank.