A Novel Gate Resource Allocation Method Using Improved PSO-Based QEA
By Wu Deng, Junjie Xu, Huimin Zhao, Yingjie Song. IEEE transactions on intelligent transportation systems (Print), 2020
296 citations recorded by the source corpus.
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.
Subfields: Aerospace Engineering, Economics and Econometrics, Transportation
View Paper: https://www.semanticscholar.org/paper/a70d45c081eed6b8ea483405cecccd53a646fd4e
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- Ali Asghar Heidari
- Huiling Chen
- Zhou Wei
- Pengjun Wang
- Majdi M. Mafarja
- Xuehua Zhao
- Hamza Turabieh
- Tansel Dökeroğlu
Citations in the corpus
- Metaphor-free dynamic spherical evolution for parameter estimation of photovoltaic modules (2021, background): “…control (Zeng et al., 2014, 2015, 2019), wind speed prediction (Chen et al., 2019d), scheduling problem (Pang et al., 2018; Zhou et al., 2018), fault diagnosis of rolling bearings (Deng et al., 2020a; Zhao et al., 2019c), and gate resource allocation (Deng et al., 2020b; W et al., 2020).”