Electronics and Electrical Engineering and Control

Arrival sequencing and scheduling based on multi-objective Imperialist competitive algorithm

  • ZHANG Junfeng ,
  • YOU Lubao ,
  • YANG Chunwei ,
  • HU Rong
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  • College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

Received date: 2020-06-22

  Revised date: 2020-07-16

  Online published: 2020-08-17

Supported by

National Natural Science Foundation of China (U1933117); Foundation of Graduate Innovation Center in NUAA (kfjj20190718)

Abstract

A new method of arrival sequencing and scheduling is proposed in this paper based on the multi-objective Imperialist competitive algorithm. This method can simultaneously consider different demands of air traffic control, airports, airlines, and the public, therefore balancing traffic demands and arrival management. The evaluation indicators of the arrival sequencing and scheduling are firstly combed and simplified, drawing on the research achievements in the field of machine scheduling, followed by the construction of a multi-objective arrival sequencing and scheduling model by combining the operating constraints. A Multi-Objective Imperialist Competitive Algorithm (MOICA) is then presented by introducing the non-dominated sorting strategy, and performance indexes are also provided to evaluate the pros and cons of Pareto solutions. Finally, a set of benchmark instances and the actual operation data of Changsha Huanghua International Airport are used to implement case simulation and verification, and comparison is made with the commonly used multi-objective algorithms such as NSGA-II or MOSA. The compared results exhibit a dominant position of the proposed MOICA with more uniform distribution, better convergence, and higher quality of the solution set. The proposed algorithm is also more efficient. Additionally, the proposed method can effectively realize the arrival sequencing and scheduling for the real case. Even when the simulation is performed at 1.8 times of the standard interval, the total delay time, total flight time, and maximum flight time are reduced by 41.2%, 11.4%, and 8.6%, respectively, relative to the actual operation.

Cite this article

ZHANG Junfeng , YOU Lubao , YANG Chunwei , HU Rong . Arrival sequencing and scheduling based on multi-objective Imperialist competitive algorithm[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021 , 42(6) : 324439 -324439 . DOI: 10.7527/S1000-6893.2020.24439

References

[1] BENNELL J A, MESGARPOUR M, POTTS C N. Airport runway scheduling[J]. Annals of Operations Research, 2013, 204(1):249-270.
[2] BEASLEY J E, KRISHNAMOORTHY M, SHARAIHA Y M, et al. Scheduling aircraft landings-the static case[J]. Transportation Science, 2000, 34(2):180-197.
[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] JI X P, CAO X B, TANG K. Sequence searching and evaluation:A unified approach for aircraft arrival sequencing and schedul-ing problems[J]. Memetic Computing, 2016, 8(2):109-23.
[5] SÖLVELING G, CLARKE J P. Scheduling of airport runway operations using stochastic branch and bound methods[J]. Transportation Research Part C:Emerging Technologies, 2014, 45:119-137.
[6] LIEDER A, BRISKORN D, STOLLETZ R. A dynamic programming approach for the aircraft landing problem with aircraft classes[J]. European Journal of Operational Research, 2015, 243(1):61-69.
[7] VADLAMANI S, HOSSEINI S. A novel heuristic approach for solving aircraft landing problem with single runway[J]. Journal of Air Transport Management, 2014, 40:144-148.
[8] 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.
[9] SAMÀ M, D'ARIANO A, D'ARIANO P, et al. Optimal aircraft scheduling and routing at a terminal control area during disturbances[J]. Transportation Research Part C:Emerging Technologies, 2014, 80(6):61-85.
[10] SALEHIPOUR A, MODARRES M, MOSLEMI NAENI L. An efficient hybrid meta-heuristic for aircraft landing problem[J]. Computers & Operations Research, 2013, 40(1):207-213.
[11] HU X B, DIPAOLO E. Binary-representation-based genetic algorithm for aircraft arrival sequencing and scheduling[J]. IEEE Transactions on Intelligent Transportation Systems, 2008, 9(2):301-310.
[12] GIRISH B G. An efficient hybrid particle swarm optimization algorithm in a rolling horizon framework for the aircraft landing problem[J]. Applied Soft Computing, 2016, 44:200-221.
[13] SAMÀ M, D'ARIANO A, D'ARIANO P, et al. Scheduling models for optimal aircraft traffic control at busy airports:Tardiness, priorities, equity and violations considerations[J]. Omega, 2017, 67:81-98.
[14] HONG Y, CHO N, KIM Y, et al. Multiobjective optimization for aircraft arrival sequencing and scheduling[J]. Journal of Air Transportation, 2017, 25(4):115-122.
[15] ZHANG J F. Multi-objective integrated arrival & departure aircraft sequencing under the influence of sequential flights[C]//Integrated Communications, Navigation, Surveillance Conference (ICNS). Piscataway:IEEE Press, 2018.
[16] MOKHTARIMOUSAVI S, RAHAMI H, KAVEH A. Multi-objective mathematical modeling of aircraft landing problem on a runway in static mode, scheduling and sequence determination using NSGA-II[J]. Applied Mathematics & Computation, 2015, 5(1):21-36.
[17] 马园园, 胡明华, 张洪海, 等. 多机场终端区进场航班协同排序方法[J]. 航空学报, 2015, 36(7):2279-2290. MA Y Y, HU M H, ZHANG H H, et al. Optimized method for collaborative arrival sequencing and scheduling in metroplex terminal area[J]. Acta Aeronautica et Astronautica Sinica, 2015, 36(7):2279-2290(in Chinese).
[18] 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(7):323717. LIU J X, JIANG H, DONG X F, et al. Dynamic collaborative sequencing method for arrival flights based on air traffic density[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(7):323717(in Chinese).
[19] HOSSEINIA S, KHALED A. A survey on the imperialist competitive algorithm metaheuristic:Implementation in engineering domain and directions for future research[J]. Applied Soft Computing, 2014, 24:1078-1094.
[20] GULSAH H, 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(1):39-48.
[21] 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 J F, ZHENG Z X, GE T T. Sequencing approach of arrival aircrafts based on composite dispatching rules[J]. Journal of Traffic and Transportation Engineering, 2017, 17(3):141-150(in Chinese).
[22] 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.
[23] ZANDIEH M, KHATAMI A R, RAHMATI S H A. Flexible job shop scheduling under condition-based maintenance:Improved version of imperialist competitive algorithm[J]. Applied Soft Computing, 2017, 58:449-464.
[24] ZHANG J F, LIU J, HU R, et al. Online four dimensional trajectory prediction method based on aircraft intent updating[J]. Aerospace Science and Technology, 2018, 77:774-787.
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