A Large Neighborhood Search Algorithm with Simulated Annealing and Time Decomposition Strategy for the Aircraft Runway Scheduling Problem

• Published in: Aerospace Vol. 10; no. 2; p. 177
• Main Authors: Su, Jiaming, Hu, Minghua, Liu, Yingli, Yin, Jianan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2023
• Subjects: Air traffic controllers; air traffic flow management; Air traffic management; Aircraft; aircraft runway scheduling problem; Airport expansion; Airports; Algorithms; Efficiency; Integer programming; large neighborhood search; Optimization; Parameter sensitivity; receding horizon control; Receding horizon principle; Runways; Scheduling; Search algorithms; Search process; Sensitivity analysis; Simulated annealing; Simulation; Solution space; Traffic flow; Transportation terminals; Traveling salesman problem; China
• ISSN: 2226-4310; 2226-4310
• DOI: 10.3390/aerospace10020177

The runway system is more likely to be a bottleneck area for airport operations because it serves as a link between the air routes and airport ground traffic. As a key problem of air traffic flow management, the aircraft runway scheduling problem (ARSP) is of great significance to improve the utilization of runways and reduce aircraft delays. This paper proposes a large neighborhood search algorithm combined with simulated annealing and the receding horizon control strategy (RHC-SALNS) which is used to solve the ARSP. In the framework of simulated annealing, the large neighborhood search process is embedded, including the breaking, reorganization and local search processes. The large neighborhood search process could expand the range of the neighborhood building in the solution space. A receding horizon control strategy is used to divide the original problem into several subproblems to further improve the solving efficiency. The proposed RHC-SALNS algorithm solves the ARSP instances taken from the actual operation data of Wuhan Tianhe Airport. The key parameters of the algorithm were determined by parametric sensitivity analysis. Moreover, the proposed RHC-SALNS is compared with existing algorithms with excellent performance in solving large-scale ARSP, showing that the proposed model and algorithm are correct and efficient. The algorithm achieves better optimization results in solving large-scale problems.