期刊
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
卷 23, 期 3, 页码 1737-1745出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2020.3025796
关键词
Logic gates; Resource management; Airports; Atmospheric modeling; Sociology; Statistics; Optimization; Gate allocation model; QEA; PSO; niche co-evolution strategy; improved rotation gates; multi-objective
资金
- National Natural Science Foundation of China [61771087, U1433124, 51879027]
- Open Project Program of the Traction Power State Key Laboratory of Southwest Jiaotong University [TPL2002]
- State Key Laboratory of Mechanical Transmissions of Chongqing University [SKLMT-KFKT-201803]
- Central University Basic Scientific Research Business Fee Project of Civil Aviation University of China [2000420534]
The paper introduces a three-objective gate allocation model to optimize passenger walking distances, balanced idle time, and efficient use of gates. An IPOQEA algorithm is proposed to efficiently solve the model, with validation on the effectiveness at Baiyun Airport.
With the continuous and rapid growth of air traffic demand, gate resource becomes a major bottleneck restricting airport development. Rational gate allocation is regarded as one of the most important means to solve this bottleneck. In this paper, in order to comprehensively considere different stakeholders, a three-objective gate allocation model is to consider a wider scope, in which the minimizing passenger walking distances, the most balanced idle time of each gate and the best full use of large gate are optimized simultaneously to improve the practical efficiency. To efficiently solve this model, an improved quantum evolutionary algorithm (QEA) based on the niche co-evolution strategy and enhanced particle swarm optimization (PSO), namely IPOQEA is designed. An IPOQEA-based gate allocation method is proposed to allocate the flights to suitable gates within different periods. Finally, the actual operation data of Baiyun Airport is used to validate the effectiveness of the proposed method. Comparison results show that the constructed model can address the passenger walking distances, robustness and costs in airport management. Moreover, the IPOQEA has better optimization ability in solving gate allocation problem. Therefore, the proposed gate allocation method has great potential for practical engineering since it can easily make decisions for airport managers.
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