To address the impact of uncertainties in the terminal area on flight arrival times, this paper proposed a two-stage stochastic programming method based on chance constraints, aiming to achieve robustness in the scheduling scheme. First, based on historical flight data, identify the uncertainty distribution of arrival times from the entry fix to the initial approach fix (IAF). Second, considering the uncertainty distribution, chance constraints are introduced to limit the probability of violating separation constraints; a two-stage stochastic programming model is then established: the first stage pertains to approach control, flights are pre-sequenced and scheduled before reaching the IAF, so as to minimize landing sequence length and flight time; the second stage pertains to final approach control, safety intervals are established to reduce landing delays on the runway. Subsequently, the Rolling Horizon Control (RHC) algorithm for stochastic programming is introduced to satisfy the real-time requirements of approach operations. Then, the model is reconstructed and solved based on the Sample Average Approximation (SAA) algorithm. Finally, the proposed method was validated using actual operational data from Guangzhou Baiyun International Airport. The results demonstrate that the proposed RHC not only ensures solution quality but also significantly enhances model-solving efficiency. Moreover, the robustness of the approach scheduling scheme is improved under the “one landing, one takeoff” and “two landings, one takeoff” operational modes. For the landing delay index, the First-Come, First-Served (FCFS) strategy results in delays 6.1 times and 9.6 times higher than those achieved by the proposed method, respectively; for the violation of separation proportion index, the FCFS strategy rates are 20% and 18.9%, whereas the proposed method maintains a rate of 3.5% in both modes. Regarding the sequence exchange number, the FCFS strategy incurs 5.2 and 5.6 exchanges, respectively, while the proposed method incurs zero exchanges in both modes.
ZHANG Jun-Feng
,
MA Zhao
,
DU Zhuo-Ming
,
HU Rong
. Dynamic Robust Scheduling of Aircraft Arrival in Multi-Runway Mixed Operation Mode[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 0
: 0
-0
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DOI: 10.7527/S1000-6893.2024.30956
[1] IKLI S, MANCEL C, MONGEAU M, et al. The aircraft runway scheduling problem: A survey[J]. Computers & Operations Research, 2021, 132: 105336.
[2] SAMà M, D’ARIANO A, PACCIARELLI D. Scheduling models for optimal aircraft traffic control at busy airports: tardiness, priorities, equity and violations considerations[J]. Omega, 2017, 67: 81-98.
[3] BENNELL J A, MESGARPOUR M, POTTS C N. Dynamic scheduling of aircraft landings[J]. European Journal of Operational Research, 2017, 258(1): 315-327.
[4] ZHANG J F, ZHAO P L, ZHANG Y, et al. Criteria selection and multi-objective optimization of aircraft landing problem[J]. Journal of Air Transport Management, 2020, 82:101734.
[5] ZHANG J F, ZHAO P L, YANG C W, et al. A new meta?heuristic approach for aircraft landing problem[J]. Transactions of Nanjing University of Aeronautics and Astronautics, 2020 , 37(2): 197?208.
[6] 刘继新,江灏,董欣放,等.基于空中交通密度的进场航班动态协同排序方法[J].航空学报,2020,41(07):285-300.
[7] HONG Y K, CHO N, KIM Y, et al. Multi-objective Optimization for Aircraft Arrival Sequencing and Scheduling[J]. Journal of Air Transportation, 2017, 25(4): 109-114.
[8] 张军峰,游录宝,杨春苇,等.基于多目标帝国竞争算法的进场排序与调度[J].航空学报,2021,42(06):475-487.
[9] TIELROOIJ M, BORST C, VAN PAASSEN M M, et al. Predicting arrival time uncertainty from actual flight information[C]//Proceedings of the 11th USA/Europe Air Traffic Management Research and Development Seminar. FAA/EUROCONTROL, 2015: 577-586.
[10] YIN J, MA Y, HU Y, et al. Delay, throughput and emission tradeoffs in airport runway scheduling with uncertainty considerations[J]. Networks and Spatial Economics, 2021, 21: 85-122.
[11] BOSSON C S, S D. Optimization of airport surface operations under uncertainty[J]. Journal of Air Transportation, 2016, 24(3): 84-92.
[12] CECEN R K. A stochastic programming model for the aircraft sequencing and scheduling problem considering flight duration uncertainties[J]. The Aeronautical Journal, 2022, 126(1304): 1736-1751.
[13] DONMEZ K. Aircraft sequencing under the uncertainty of the runway occupancy times of arrivals during the backtrack procedure[J]. The Aeronautical Journal, 2023, 127(1310): 562-580.
[14] KHASSIBA A, CAFIERI S, BASTIN F, et al. Two-stage stochastic programming models for the extended aircraft arrival management problem with multiple pre-scheduling points[J]. Transportation Research Part C: Emerging Technologies, 2022, 142: 103769.
[15] KHASSIBA A, BASTIN F, GENDRON B, et al. Extended aircraft arrival management under uncertainty: A computational study[J]. Journal of Air Transportation, 2019, 27(3): 131-143.
[16] KHASSIBA A, BASTIN F, CAFIERI S, et al. Two-stage stochastic mixed-integer programming with chance constraints for extended aircraft arrival management[J]. Transportation Science, 2020, 54(4): 897-919.
[17] CHEN X, YU H, CAO K, et al. Uncertainty-aware flight scheduling for airport throughput and flight delay optimization[J]. IEEE Transactions on Aerospace and Electronic Systems, 2019, 56(2): 853-862.
[18] NG K K H, LEE C K M, CHAN F T S, et al. Robust aircraft sequencing and scheduling problem with arrival/ departure delay using the min-max regret approach[J]. Transportation Research Part E: Logistics and Transportation Review, 2017, 106: 115-136.
[19] KAPOLKE M, FURSTENAU N, HEIDT A, et al. Pre-tactical optimization of runway utilization under uncertainty[J]. Journal of Air Transport Management, 2016, 56: 48-56.
[20] ESCHE E, YOU B, REPKE J U. Optimal design via chance-constrained or two-stage stochastic programming [M] // Computer Aided Chemical Engineering. Elsevier, 2019, 47: 169-174.
[21] KIM S, PASUPATHY R, HENDERSON S G. A guide to sample average approximation[J]. Handbook of simulation optimization, 2015: 207-243.
[22] WU L J, ZHAN Z H, HU X M, et al. Multi-runway aircraft arrival scheduling: a receding horizon control based ant colony system approach[C]//2019 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2019: 538-545.
[23] ZHANG J F, PENG Z H, YANG C W, et al. Data-driven flight time prediction for arrival aircraft within the terminal area. IET Intell. Transp. Syst.1–13 (2021).
[24] GONZE F, HUENS E, JUNGERS R M, et al. Probabilistic occupancy counts and flight criticality measures in air traffic management[J]. Journal of Air Transportation, 2018, 26(3): 94-103.
[25] XIA P, ZHANG L, LI F. Learning similarity with cosine similarity ensemble[J]. Information sciences, 2015, 307: 39-52.
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