|Table of Contents|

Coordinated scheduling model of arriving aircraft at large airport(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2022年01期
Page:
205-215
Research Field:
交通运输规划与管理
Publishing date:

Info

Title:
Coordinated scheduling model of arriving aircraft at large airport
Author(s):
JIANG Yu1 LIU Zhen-yu1 HU Zhi-tao1 WU Wei-wei1 WANG Zhe2
(1. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, Jiangsu, China; 2. Beijing Capital International Airport Co., Ltd., Beijing 100621, China)
Keywords:
air transportation aircraft operation coordinated scheduling improved genetic algorithm reverse optimization gate adjustment
PACS:
V355.1
DOI:
10.19818/j.cnki.1671-1637.2022.01.017
Abstract:
The problem of coordinated scheduling of arriving aircraft at large airports was studied to reduce the total delay and taxi times of arriving aircraft. The minimum weighted sum of runway sequencing time span and total delay time, the fewest number of flights assigned to remote gates, and the shortest taxi time of arriving aircraft were taken as the objective functions, respectively. A forward coordinated scheduling model for the systems of runway, gate, and taxiway was constructed. A gate re-adjustment model was used to perform the reverse optimization of the taxiway by adjusting the gate assignments of aircrafts with long taxi times. A genetic algorithm(GA)with improved gene coding was designed to prevent the generation of unfeasible solutions and to improve efficiency. Simulation results show that, compared with the first-come-first-serve strategy, the improved GA reduces the runway sequencing time of arriving aircraft by 20 s and the total delay time by 21% from 254 350 s to 199 760 s, respectively. Compared with the ant colony optimization, the improved GA reduces the total delay time by 20 060 s(9%)and produces a smoother iteration curve. After 12 iterations of the improved GA, all arrival aircrafts can be assigned to bridge gates. The total taxi time of 18 arriving aircrafts decreases by 9% from 4 575 s to 4 145 s, and only 3 taxi conflicts occur. 11 aircrafts choose the shortest routes, and only 3 aircrafts have extra taxi time of more than 40 s. After gate adjustment, the total extra taxi time decreases by 58 s or 27%. Therefore, the proposed coordinated scheduling model for arriving aircraft can improve the operational efficiency of large airports and provide decision-making support for airfield resource management.5 tabs, 11 figs, 31 refs.

References:

[1] MAHARJAN B, MATIS T I. Multi-commodity flow network model of the flight gate assignment problem[J]. Computers and Industrial Engineering, 2012, 63(4): 1135-1144.
[2] DIEPEN G, AKKER J M, HOOGEVEEN J A, et al. Finding a robust assignment of flights to gates at Amsterdam Airport Schiphol[J]. Journal of Scheduling, 2012, 15(6): 703-715.
[3] DORNDORF U, JAEHN F, PESCH E. Flight gate assignment and recovery strategies with stochastic arrival and departure times[J]. OR Spectrum, 2017, 39(1): 65-93.
[4] CASTAING J, MUKHERJEE I, COHN A, et al. Reducing airport gate blockage in passenger aviation: models and analysis[J]. Computers and Operations Research, 2016, 65: 189-199.
[5] KIM S H, FERON E. Robust gate assignment against gate conflicts[J]. Journal of Air Transportation, 2017, 25(3): 87-94.
[6] AOUN O, EL AFIA A. Using Markov decision processes to solve stochastic gate assignment problem[C]∥IEEE. 2nd IEEE International Conference on Logistics Operations Management. New York: IEEE, 2014: 42-47.
[7] 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.
[8] DENG Wu, ZHAO Hui-min, YANG Xin-hua, et al. Study on an improved adaptive PSO algorithm for solving multi-objective gate assignment[J]. Applied Soft Computing, 2017, 59: 288-302.
[9] DENG Wu, SUN Meng, ZHAO Hui-min, et al. Study on an airport gate assignment method based on improved ACO algorithm[J]. Kybernetes, 2018, 47(1): 20-43.
[10] YU Sheng-peng, CAO Xian-bin, ZHANG Jun. A real-time schedule method for Aircraft Landing Scheduling problem based on Cellular Automation[J]. Applied Soft Computing, 2011, 11(4): 3485-3493.
[11] 张军峰,郑志祥,葛腾腾.基于复合分派规则的进场航班排序方法[J].交通运输工程学报,2017,17(3):141-150.ZHANG Jun-feng, ZHENG Zhi-xiang, GE Teng-teng. Sequencing approach of arrival aircrafts based on composite dispatching rules[J]. Journal of Traffic and Transportation Engineering, 2017, 17(3): 141-150.(in Chinese)
[12] 王世豪,杨红雨,李玉贞,等.基于精英存档自适应微分进化算法的多跑道独立进近排序[J].工程科学与技术,2017,49(3):153-161.WANG Shi-hao, YANG Hong-yu, LI Yu-zhen, et al. Multi-runways independent approach scheduling using self-adaptive differential evolution algorithm with elite archive[J]. Advanced Engineering Sciences, 2017, 49(3): 153-161.(in Chinese)
[13] HANCERLIOGULLARI G, RABADI G, AL-SALEM A H, et al. Greedy algorithms and metaheuristics for a multiple runway combined arrival-departure aircraft sequencing problem[J]. Journal of Air Transport Management, 2013, 32: 39-48.
[14] BALL M O, BERARDINO F, HANSEN M. The use of auctions for allocating airport access rights[J]. Transportation Research Part A: Policy and Practice, 2018, 114: 186-202.
[15] LANDRY S J, CHEN X W, NOF S Y. A decision support methodology for dynamic taxiway and runway conflict prevention[J]. Decision Support Systems, 2013, 55(1): 165-174.
[16] 冯 程,胡明华,丛 玮.基于滚动时域算法的航班滑行路径优化模型[J].航空计算技术,2014,44(4):80-85.FENG Cheng, HU Ming-hua, CONG Wei. Optimization model of taxiway routing based on receding horizon[J]. Aeronautical Computing Technique, 2014, 44(4): 80-85.(in Chinese)
[17] LUO Xiao, TANG Yong, WU Hong-gang, et al. Real-time adjustment strategy for conflict-free taxiing route of A-SMGCS aircraft on airport surface[C]∥IEEE. International Conference on Mechatronics and Automation(ICMA).New York: IEEE, 2015: 929-934.
[18] ZHANG Tian-ci, DING Meng, WANG Bang-feng, et al. Conflict-free time-based trajectory planning for aircraft taxi automation with refined taxiway modeling[J]. Journal of Advanced Transportation, 2016, 50(3): 326-347.
[19] ZHANG Tian-ci, DING Meng, ZUO Hong-fu. Improved approach for time-based taxi trajectory planning towards conflict-free, efficient and fluent airport ground movement[J]. IET Intelligent Transport Systems, 2018, 12(10): 1360-1368.
[20] BADRINATH S, BALAKRISHNAN H. Control of a non-stationary tandem queue model of the airport surface[C]∥IEEE. 2017 American Control Conference(ACC). New York: IEEE. 2017: 655-661.
[21] WEISZER M, CHEN J, LOCATELLI G. An integrated optimization approach to airport ground operations to foster sustainability in the aviation sector[J]. Applied Energy, 2015, 157(1): 567-582.
[22] BROWNLEE A E I, WEISZER M, CHEN J, et al. A fuzzy approach to addressing uncertainty in Airport Ground Movement optimization[J]. Transportation Research Part C: Emerging Technologies, 2018, 92: 150-175.
[23] CHEN J, WEISZER M, ZAREIAN E, et al. Multi-objective fuzzy rule-based prediction and uncertainty quantification of aircraft taxi time[C]∥IEEE. 20th International Conference on Intelligent Transportation Systems. New York: IEEE, 2017: 1-5.
[24] GUÉPET J, BRIANT O, GAYON J P, et al. The aircraft ground routing problem: Analysis of industry punctuality indicators in a sustainable perspective[J]. European Journal of Operational Research, 2016, 248(3): 827-839.
[25] BENLIC U, BROWNLEE A E I, BURKE E K. Heuristic search for the coupled runway sequencing and taxiway routing problem[J]. Transportation Research Part C: Emerging Technologies, 2016, 71: 333-355.
[26] YU C H, LAU H Y K. Integrated optimization of airport taxiway and runway scheduling[J]. Journal of Automation and Control Engineering, 2014, 2(4): 338-342.
[27] YU Chu-hang, ZHANG Dong, LAU H Y K. A heuristic approach for solving an integrated gate reassignment and taxi scheduling problem[J]. Journal of Air Transport Management, 2017, 62: 189-196.
[28] GUÉPET J, BRIANT O, GAYON J P, et al. Integration of aircraft ground movements and runway operations[J]. Transportation Research Part E: Logistics and Transportation Review, 2017, 104: 131-149.
[29] MURÇA M C R. A robust optimization approach for airport departure metering under uncertain taxi-out time predictions[J]. Aerospace Science and Technology, 2017, 68: 269-277.
[30] SIMAIAKIS I, KHADILKAR H, BALAKRISHNAN H, et al. Demonstration of reduced airport congestion through pushback rate control[J]. Transportation Research Part A: Policy and Practice, 2014, 66: 251-267.
[31] 余朝军,江 驹,徐海燕,等.基于改进遗传算法的航班-登机口分配多目标优化[J].交通运输工程学报,2020,20(2):121-130.YU Chao-jun, JIANG Ju, XU Hai-yan, et al. Multi-objective optimization of flight-gate assignment based on improved genetic algorithm[J]. Journal of Traffic and Transportation Engineering, 2020, 20(2): 121-130.(in Chinese)

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Last Update: 2022-03-20