Computer Science ›› 2020, Vol. 47 ›› Issue (8): 278-283.doi: 10.11896/jsjkx.190400154

Previous Articles     Next Articles

Study on Optimal Scheduling of Gate Based on Mixed Integer Programming

ZHANG Hong-ying1, SHEN Rong-miao1, LUO Qian2   

  1. 1 College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China
    2 The Second Research Institute of Civil Aviation Administration of China, Chengdu 610041, China
  • Online:2020-08-15 Published:2020-08-10
  • About author:ZHANG Hong-ying, born in 1978, Ph.D, professor, postgraduate supervisor.Her main research interests include airport intelligence and automation technology.
  • Supported by:
    This work was supported by the Key Projects of the Civil Aviation Joint Fund of the National Natural Science Foundation of China(U1533203).

Abstract: In order to alleviate effectively the current situation of airport aircraft delay, the optimal scheduling of airport gate is studied.By deeply analyzing the characteristics of airport ground operation, considering the constraint restrictions such as aircraft model matching, buffer time and aircraft conflict, scientifically and reasonably weighing the various interest needs of the airport, this paper proposes a mixed integer programming model to optimize the gate scheduling problem, the main goal is to ensure the safe operation of the aircraft under the premise, so that the total flight delay time is the shortest.The probability distribution function is introduced to avoid the occurrence of aircraft conflict.Combining with the basic theory of multi-objective optimization and branching definition algorithm, the optimal assignment schemeis sought.The simulation results show that the model optimizes the timing of the expected inbound aircraft in the airport, adjusts the position allocation conflict by optimizing the scheduling scheme, and gets the optimal allocation scheme.The algorithm can reduce the search space, improve the efficiency of the solution, significantly reduce the total delay time, and improve the utilization rate of airport gate resources.Compared with heuristic algorithm, the aircraft delay is reduced by 2.4%, and the proposed method caneffectively reduce the delay rate of airport ground flight.

Key words: Branch-and-cut method, Flightdelay, Gate scheduling, Mixed integer programming, Probability distribution function

CLC Number: 

  • TP391.9
[1]DENG W, SUN M, ZHAO H, et al.Study on an airport gate assignment method based on improved ACOalgorithm[J].Kybernetes, 2018, 47(1):20-43.
[2]LIU L H, ZHANG Y P, XING Z W, et al.Optimization of aircraft pushback decision based on discrete differential evolution[J].Journal of Transportation Systems Engineering and information technology, 2016, 16(6):196-203.
[3]ZHANG J R, WANG G, TONG S Y.Research on Flight First Service Model and Algorithms for the Gate Assignment Problem[J].CMC-Computers, Materials & Continua, 2019, 61(3):1091-1104.
[4]ZHAO J M, WU W J, LIU Z M, et al.Airport gate assignment problem with deep reinforcement learning[J].High Technology Letters, 2020, 26(1):102-107.
[5]LI Z, WANG Z X, SUI H.Research on the Evaluation IndexSystem of Operational Support Effectiveness of Military Airport Facilities[J].Journal of Chongqing University of Technology (Natural Science), 2018, 32(9):209-216.

[6]DORNDORF U, JAEHN F, PESCH E.Flight gate assignment and recovery strategies with stochastic arrival and departure times[J].Or Spectrum, 2016, 39(1):1-29.
[7]BOURAS A, GHALEB M A, SURYAHATMAJA U S, et al.The airport gate assignment problem:a survey[J].Scientific World Journal, 2014, 2014(6):9165-9172.
[8]CHENG C H, HO S C, KWAN C L.The use of meta-heuristics for airport gate assignment[J].Expert Systems with Applications, 2012, 39(16):12430-12437.
[9]YU C, ZHANG D, LAU H Y K.An adaptive large neighborhoodsearch heuristic for solving a robust gate assignmentproblem[J].Expert Systems with Applications, 2017, 84:143-154.
[10]ZHANG J, CHEN Q, SUN G, et al.Disruption Scheduling ofAirport Gate Based on Tabu Search Algorithm[C]∥Control Conference, 2014(CCC).IEEE, 2014:84-88.
[11]NEUMAN U M, ATKIN J A D.Airport Gate Assignment Considering Ground Movement[J].Lecture Notes in Computer Science, 2013, 8197:184-198.
[12]LI D.Research on batch scheduling problem with non-identical job sizes using ant colony optimization algorithm[D].Hefei:Anhui University, 2014.
[13]LI Y L, LI Y.Aircraft stands assignment optimization based on variable tabu length[J].Journal of Computer Applications, 2016, 36(10):2940-2944.
[14]WANG Y H, ZHU J F, ZHU B, et al.Mixed collection planning method for seat allocation in busy airports[J].Journal of Wuhan University of Technology(Information & Management Engineering), 2015, 37(4):427-431.
[15]BOURAS A, GHALEB M A, SURYAHATMAJA U S, et al.The Airport Gate Assignment Problem:A Survey[J].Scientific World Journal, 2014, 27(6):9165-9172.
[16]PANG M B, ZHANG S L, LI C X.Bi-level programming of urban bus stop locating[J].Journal of Highway and Transportation Research and Development, 2013, 30(3):118-124.
[17]PREM KUMAR V, BIERLAIRE M.Multi-objective airportgate assignment problem in planning and operations[J].Journal of Advanced Transportation, 2015, 48(7):902-926.
[18]SANG H K, FERON E.Impact of Gate Assignment on Departure Metering[J].IEEE Transactions on Intelligent Transportation Systems, 2014, 15(2):699-709.
[19]GUPET J, ACUNA-AGOST R, BRIANT O, et al.Exact and Heuristic Approaches to the Airport Stand Allocation Problem[J].European Journal of Operational Research, 2015, 246(2):597-608.
[1] ZHENG Fei-feng, JIANG Juan, MEI Qi-huang. Study on Stowage Optimization in Minimum Container Transportation Cost [J]. Computer Science, 2019, 46(6): 239-245.
[2] ZHAO Ye,ZHOU Chang,WANG Chang. Feature Preserved Mesh Simplification Algorithm Based on Stochastic Sampling [J]. Computer Science, 2011, 38(5): 249-251.
Full text



No Suggested Reading articles found!