This study aims to propose a new airport gate assignment method to effectively improve the comprehensive operation capacity and efficiency of hub airport. Gate assignment is one of the most important tasks for airport ground operations, which assigns appropriate airport gates with high efficiency reasonable arrangement.
In this paper, on the basis of analyzing the characteristics of airport gates and flights, an efficient multi-objective optimization model of airport gate assignment based on the objectives of the most balanced idle time, the shortest walking distances of passengers and the least number of flights at apron is constructed. Then an improved ant colony optimization (ICQACO) algorithm based on the ant colony collaborative strategy and pheromone update strategy is designed to solve the constructed model to fast realize the gate assignment and obtain a rational and effective gate assignment result for all flights in the different period.
In the designed ICQACO algorithm, the ant colony collaborative strategy is used to avoid the rapid convergence to the local optimal solution, and the pheromone update strategy is used to quickly increase the pheromone amount, eliminate the interference of the poor path and greatly accelerate the convergence speed.
The actual flight data from Guangzhou Baiyun airport of China is selected to verify the feasibility and effectiveness of the constructed multi-objective optimization model and the designed ICQACO algorithm. The experimental results show that the designed ICQACO algorithm can increase the pheromone amount, accelerate the convergence speed and avoid to fall into the local optimal solution. The constructed multi-objective optimization model can effectively improve the comprehensive operation capacity and efficiency. This study is a very meaningful work for airport gate assignment.
An efficient multi-objective optimization model for hub airport gate assignment problem is proposed in this paper. An improved ant colony optimization algorithm based on ant colony collaborative strategy and the pheromone update strategy is deeply studied to speed up the convergence and avoid to fall into the local optimal solution.
The authors would like to thank all the reviewers for their constructive comments. This research was supported by the National Natural Science Foundation of China (U1433124, 61771087, 51475065, 51605068), Open Project Program of Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis (HCIC201601), Open Project Program of Sichuan Provincial Key Lab of Process Equipment and Control (GK201613), Natural Science Foundation of Liaoning Province (2015020013), Science and Technology Project of Liaoning Provincial Department of Education (JDL2016030). The program for the initialization, study, training, and simulation of the proposed algorithm in this article was written with the tool-box of MATLAB 2010b produced by the Math-Works, Inc.
Conflict of interest: The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
Deng, W., Sun, M., Zhao, H., Li, B. and Wang, C. (2018), "Study on an airport gate assignment method based on improved ACO algorithm", Kybernetes, Vol. 47 No. 1, pp. 20-43. https://doi.org/10.1108/K-08-2017-0279Download as .RIS
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