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Predictive and prescriptive analytics for robust airport gate assignment planning in airside operations under uncertainty
Transportation Research Part E: Logistics and Transportation Review ( IF 8.3 ) Pub Date : 2025-01-18 , DOI: 10.1016/j.tre.2025.103963
Chenliang Zhang, Zhongyi Jin, Kam K.H. Ng, Tie-Qiao Tang, Fangni Zhang, Wei Liu

With the increasing demand for air transport, numerous airports have exceeded their available capacity, resulting in more frequent congestion and disruptions. Therefore, airport gate assignment plans must prioritise robustness to alleviate congestion, absorb disruptions, and maintain high service levels. Given the uncertainties in airside operations, providing robust decisions is challenging. To address this issue, we employ two prescriptive analytics approaches to develop airport gate assignment plans. These approaches leverage historical data, auxiliary data, and machine learning (ML) methods to enhance decision effectiveness and robustness. Initially, we adopt a predict-then-optimise approach, utilising ML methods to predict aircraft arrival times. These predictions are then used as input for a deterministic model of the airport gate assignment problem (AGAP). Subsequently, we explore an estimate-then-optimise approach. In this approach, we first estimate the distribution of uncertain aircraft arrival times using ML methods. Then, we solve the two-stage stochastic programming model for the AGAP based on the estimated distribution. Given the complexity of the estimate-then-optimise approach, we develop an effective scenario selection strategy, the cluster-based scenario reduction (CSR) method, to maintain tractability while ensuring decision performance. Concurrently, we develop an efficient exact solution method, the Benders-based branch-and-cut (BBC) method, to effectively handle larger and more complex test instances. Numerical experiments using real-world data from Xiamen Gaoqi International Airport demonstrate the effectiveness of the CSR and BBC methods. The CSR method performs better with a smaller sample size, while the BBC method significantly enhances computational performance compared to commercial solvers. These proposed methods improve the tractability and scalability of the estimate-then-optimise approach. Notably, the estimate-then-optimise approach outperforms the predict-then-optimise approach driven by the same ML method. Furthermore, we find that estimate-then-optimise approaches, supported by well-performing ML methods and scenario selection strategies, provide superior performance compared to other optimisation approaches.

中文翻译:


在不确定性下为空侧运营提供稳健的机场登机口分配规划的预测性和规范性分析



随着航空运输需求的增加,许多机场已经超出了其可用容量,导致更频繁的拥堵和中断。因此,机场登机口分配计划必须优先考虑稳健性,以缓解拥堵、吸收中断并保持高服务水平。鉴于空侧运营的不确定性,提供稳健的决策具有挑战性。为了解决这个问题,我们采用两种规范性分析方法来制定机场登机口分配计划。这些方法利用历史数据、辅助数据和机器学习 (ML) 方法来提高决策的有效性和稳健性。最初,我们采用先预测后优化的方法,利用 ML 方法来预测飞机到达时间。然后将这些预测用作机场登机口分配问题 (AGAP) 的确定性模型的输入。随后,我们探索了一种先估计后优化的方法。在这种方法中,我们首先使用 ML 方法估计不确定飞机到达时间的分布。然后,我们根据估计的分布求解 AGAP 的两阶段随机规划模型。鉴于先估计后优化方法的复杂性,我们开发了一种有效的情景选择策略,即基于集群的情景缩减 (CSR) 方法,以在确保决策性能的同时保持可处理性。同时,我们开发了一种高效的精确求解方法,即基于 Benders 的分支和切割 (BBC) 方法,以有效处理更大、更复杂的测试实例。使用来自厦门高崎国际机场的真实世界数据的数值实验证明了 CSR 和 BBC 方法的有效性。 CSR 方法在样本量较小时性能更好,而与商业求解器相比,BBC 方法显著提高了计算性能。这些提出的方法提高了 estimate-then-optimize 方法的可处理性和可扩展性。值得注意的是,估计后优化方法优于由相同 ML 方法驱动的预测后优化方法。此外,我们发现,与其他优化方法相比,在性能良好的 ML 方法和场景选择策略的支持下,估计后优化方法提供了卓越的性能。
更新日期:2025-01-18
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