交通运输系统工程与信息

交通运输系统工程与信息 ›› 2025, Vol. 25 ›› Issue (3): 321-334.DOI: 10.16097/j.cnki.1009-6744.2025.03.029

• 系统工程理论与方法 • 上一篇    下一篇

自主取消航班下进离场时隙二次分配的双目标优化

陈振坤，陈可嘉*   

  1. 福州大学，经济与管理学院，福州350108
  • 收稿日期:2025-01-24 修回日期:2025-03-17 接受日期:2025-03-20 出版日期:2025-06-25 发布日期:2025-06-22
  • 作者简介:陈振坤(1993—)，男，河南商丘人，博士生。
  • 基金资助:
    国家社会科学基金(18BGL003)。

Bi-objective Optimization for Slot Secondary Allocation of Arrival and Departure Flights Under Autonomous Cancellations

CHEN Zhenkun, CHEN Kejia*   

  1. School of Economics and Management, Fuzhou University, Fuzhou 350108, China
  • Received:2025-01-24 Revised:2025-03-17 Accepted:2025-03-20 Online:2025-06-25 Published:2025-06-22
  • Supported by:
    National Social Science Foundation of China(18BGL003)。

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摘要： 为解决航空公司自主取消航班下的进离场时隙二次分配问题，兼顾航空公司和旅客的诉求，本文以航空公司总延误成本和旅客总延误时间最小为目标，建立双目标优化模型。引入参数λ权衡进离场航班延误成本函数的差异，并在约束中区分不同机型航班的周转时间。针对模型特点，采用实数编码，取消航班对应的基因位置返回-1，并在遗传操作中引入学习机制，对支配个体和非支配个体设计不同的交叉变异概率，对精英个体引入Q-learning驱动的通用变邻域搜索策略。3组算例的实验结果表明，改进算法的求解时间分别为29.34，58.61，125.21 s，求解方案分别有9，8，8个。相较于先到先服务方法，航空公司总延误成本分别减少了14.85%、8.47%和9.18%，旅客总延误时间分别减少了1.03%、5.31%和4.68%。相比不取消策略，取消策略下航空公司总延误成本分别减少7.04%、9.38%和11.96%，旅客总延误时间分别增加0.95%、1.21%和1.70%。本文建立的双目标优化模型和提出的改进算法能够降低航空公司延误成本，同时兼顾旅客利益，为自主取消航班下进离场时隙二次分配提供决策支持。

关键词: 航空运输, 进离场时隙二次分配, 改进NSGA-II, 自主取消航班, Q-learning

Abstract: This paper focuses on optimizing the secondary allocation of arrival and departure slots in the context of airlines autonomously canceling flights. A bi-objective optimization model is developed to minimize both the total delay costs of airlines and the total delay time of passengers, effectively addressing the needs of both stakeholders. A parameter λ is introduced to balance the discrepancies in the delay cost functions for arrival and departure flights. Additionally, constraints are implemented to differentiate the turnaround time for flights based on the types of aircraft. According to the model's characteristics, real number coding is utilized, and the gene position corresponding to the canceled flight is represented by-1. Furthermore, a learning mechanism is integrated into the genetic operations. Distinct crossover and mutation probabilities are established for both dominated and non-dominated individuals, and a Q-learning-driven general variable neighborhood search strategy is implemented for elite individuals. The experimental results from three sets of examples indicate that the solving times of the improved algorithm were 29.34 s, 58.61 s, and 125.21 s, resulting in 9, 8, and 8 optimal schedules, respectively. In comparison to the first come first served (FCFS) method, the total delay costs of airlines were reduced by 14.85%, 8.47%, and 9.18%. Additionally, the total delay times of passengers decreased by 1.03%, 5.31%, and 4.68%. Compared with the non-cancellation strategy, the total delay costs of airlines under the cancellation strategy decreased by 7.04%, 9.38% and 11.96%, and the total delay time of passengers increased by 0.95%, 1.21% and 1.70%, respectively. The bi-objective optimization model developed in this paper, along with the proposed enhanced algorithm, effectively reduces airlines' delay costs while considering passenger interests. This approach offers valuable decision-making support for the slot secondary allocation of arrival and departure flights under autonomous cancellations.

Key words: air transportation, secondary allocation of arrival and departure slots, improved NSGA-II, flights under autonomous cancellations, Q-learning

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