航班延误后多航班间车辆调度优化算法和实现

doi:10.3969/j.issn.1674-0696.2020.10.02

重庆交通大学学报（自然科学版） ›› 2020, Vol. 39 ›› Issue (10): 5-9.DOI: 10.3969/j.issn.1674-0696.2020.10.02

• 交通+大数据人工智能 • 上一篇下一篇

航班延误后多航班间车辆调度优化算法和实现

赵桂红1, 秦臻1，李建伏2

(1. 中国民航大学经济与管理学院，天津 300300； 2. 中国民航大学计算机科学与技术学院，天津 300300)

收稿日期:2018-12-24 修回日期:2019-03-09 出版日期:2020-10-30 发布日期:2020-11-03
作者简介:赵桂红 (1968—)，女，黑龙江鸡西人，教授，主要从事航空运输系统优化方面的研究。E-mail: hcaiczhao@163.com
基金资助:
国家自然科学基金项目（61103005）；中国民航局科技创新项目（MHRD20130216）

Optimization Algorithm and Implementation of Dispatched Vehicles between Several Flights in Condition of Flights Delay

ZHAO Guihong1, QIN Zhen1, LI Jianfu2

(1. College of Economics and Management, Civil Aviation University of China, Tianjin 300300, China; 2. College of Computer Science and Technology, Civil Aviation University of China, Tianjin 300300, China)

Received:2018-12-24 Revised:2019-03-09 Online:2020-10-30 Published:2020-11-03

摘要/Abstract

摘要： 停机坪作业安排不当是导致机场地面延误的原因之一，因此合理安排作业流程，缩短停机坪作业时间具有十分重要的意义。将仿生学微粒群算法引入到这种调度问题的解决方案中，结合实际的停机坪作业情况，通过合理安排服务车辆来减少总体的服务时间。基于微粒群算法的思想，研究车辆个体的最优位置和全局最优位置，从而得到每个车辆应服务的航班，再将航班进行排序便可得到各个车辆的服务流程，服务时间最长车辆所需时间即为该种服务车辆的最优时间。建立车辆问题的数学模型，在此基础上运用微粒群算法求解，通过仿真测算的结果表明该方法的可行性。通过优化，缩短了车辆服务时间，进而减少了停机坪作业时间，能够更好的保障航班准时出港。

关键词: 航空运输工程, 车辆调度优化, 微粒群算法, 服务时间, 航班延误

Abstract: The improper arrangement of apron operation is one of the reasons for airport ground delay. Therefore, it is of great significance to reasonably arrange the operation process and shorten the operation time of the apron. The bionic particle swarm optimization (PSO) algorithm was introduced into the solution of this kind of scheduling problem. Combined with the actual apron operation situation, the overall service time was reduced by reasonably arranging service vehicles. Based on the idea of PSO algorithm, the optimal location of individual vehicle and the global optimal location were studied, and the flights that each vehicle should serve were obtained. Then the service flow of each vehicle could be obtained by sorting the flights. The longest service time of the vehicle was the optimal time of this kind of service vehicle. The mathematical model of vehicle problem was established, and the PSO algorithm was used to solve the problem. The simulation results show that the proposed method is feasible. Through the optimization, the vehicle service time is shortened, and the apron operation time is reduced, which can better guarantee the flight departure on time.

Key words: air transport engineering, optimizing vehicle dispatching, particle swarm optimization (PSO) algorithm, service time, flight delay

中图分类号:

U8
V351.392

赵桂红1, 秦臻1，李建伏2. 航班延误后多航班间车辆调度优化算法和实现[J]. 重庆交通大学学报（自然科学版）, 2020, 39(10): 5-9.

ZHAO Guihong1, QIN Zhen1, LI Jianfu2. Optimization Algorithm and Implementation of Dispatched Vehicles between Several Flights in Condition of Flights Delay[J]. Journal of Chongqing Jiaotong University(Natural Science), 2020, 39(10): 5-9.

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航班延误后多航班间车辆调度优化算法和实现

Optimization Algorithm and Implementation of Dispatched Vehicles between Several Flights in Condition of Flights Delay

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