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Supervised by Ministry of Industry and Information Technology of The People's Republic of China Sponsored by Harbin Institute of Technology Editor-in-chief Yu Zhou ISSNISSN 1005-9113 CNCN 23-1378/T

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Related citation:Yaping Zhang,Ye Chen,Yu Zhang,Jian Mao.Improved Ant Colony Algorithm for Vehicle Scheduling Problem in Airport Ground Service Support[J].Journal of Harbin Institute Of Technology(New Series),2023,30(1):1-12.DOI:10.11916/j.issn.1005-9113.21021.
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Improved Ant Colony Algorithm for Vehicle Scheduling Problem in Airport Ground Service Support
Author NameAffiliation
Yaping Zhang School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150001, China 
Ye Chen School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150001, China 
Yu Zhang School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
Beijing E-hualu Information Technology Co.Ltd., Beijing 100043, China 
Jian Mao Chengdu Civil Aviation Information Technology Co.Ltd., Chengdu 610041, China 
Abstract:
Support vehicles are part of the main body of airport ground operations, and their scheduling efficiency directly impacts flight delays. A mathematical model is constructed and the responsiveness of support vehicles for current operational demands is proposed to study optimization algorithms for vehicle scheduling. The model is based on the constraint relationship of the initial operation time, time window, and gate position distribution, which gives an improvement to the ant colony algorithm (ACO). The impacts of the improved ACO as used for support vehicle optimization are compared and analyzed. The results show that the scheduling scheme of refueling trucks based on the improved ACO can reduce flight delays caused by refueling operations by 56.87%, indicating the improved ACO can improve support vehicle scheduling. Besides, the improved ACO can jump out of local optima, which can balance the working time of refueling trucks. This research optimizes the scheduling scheme of support vehicles under the existing conditions of airports, which has practical significance to fully utilize ground service resources, improve the efficiency of airport ground operations, and effectively reduce flight delays caused by ground service support.
Key words:  airport surface traffic  ground service  support vehicle scheduling  topology model  improved ant colony algorithm  response value
DOI:10.11916/j.issn.1005-9113.21021
Clc Number:V351, U491
Fund:
Descriptions in Chinese:
  

机场地面服务保障车辆排班问题的改进蚁群算法

张亚平1,陈烨1,张宇2,毛健3,罗谦3

(1,哈尔滨工业大学 交通科学与工程学院,哈尔滨 150001;2,北京易华录信息技术股份有限公司,北京 100043;3,民航成都信息技术有限公司,成都 610041)

摘要:保障车辆是机场地服作业的主体之一,其排班效率会直接影响航班的延误水平。为了研究保障车辆排班的优化算法,在保障车辆开始作业时刻与作业时间窗、停机位分布之间的约束关系的基础上,构建了排班问题的数学模型,提出了保障车辆对当前保障作业需求的响应能力值,基于响应能力值改进了蚁群算法。以机场加油车排班为例,应用改进蚁群算法求解了实例,并且对比分析了该算法的效果。结果显示:改进蚁群算法得到的加油车排班方案能使因加油作业造成的航班延误下降56.87%,证明改进蚁群算法能较好地解决保障车辆排班问题;基于响应能力值的蚁群算法获得了跳出局部最优的能力,在平衡加油车工时方面效果尤为明显。本文研究成果可用于优化机场现有条件下的保障车辆排班方案,对充分利用地面服务保障资源、提高机场场面运行效率和有效减少因地面服务保障造成的航班延误等具有一定的现实意义。

关键词:机场场面交通;地面服务;保障车辆排班;拓扑模型;改进蚁群算法;响应能力值

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