Modeling for Decision Support of Flight Ground Support Process

doi:10.16182/j.issn1004731x.joss.23-0870

Abstract

Abstract:

Aiming at the problems of insufficient decision-making ability and low operational efficiency of the flight ground support process, a decision support model of the flight ground support process based on the department of defense architecture framework (DoDAF) is proposed. Starting from the support operation, support resources, and the relationship between them, the quantitative description of the flight ground support process is performed. DoDAF and the model-based systems engineering (MBSE) modeling method are combined to establish a decision support model of the flight ground support process. The decision utility function is established to analyze the utility value of the comprehensive support of each link of flight support. The actual data are compared with the data obtained from the decision support model. The results show that the mean absolute error and the root mean square error between the off-block time after the decision and the target off-block time are reduced by 1.42 min and 0.95 min, respectively, compared to those between the actual off-block time and the target off-block time. For flights of different aircraft types, the relative errors between the support service time and the planned support service time after the decision are reduced by 31%, 17%, and 42% compared with those before the decision, and the relationship between support time and support utility can be obtained, which proves the feasibility and effectiveness of the proposed decision model.

Key words: flight ground support process, decision support, department of defense architecture framework, model-based systems engineering, system modeling

CLC Number:

TP391.9

Xing Zhiwei, Yu Ruiwen, Li Biao, Chen Zhaoxin. Modeling for Decision Support of Flight Ground Support Process[J]. Journal of System Simulation, 2024, 36(11): 2552-2565.

Figures/Tables 18

Fig. 1

Fig. 2

Fig. 3

Table 1

Table 2

Table 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Table 4

Fig. 10

Table 5

Table 6

Table 7

Fig. 11

References 25

1	Liu Chang, Chen Yanru, Chen Fenghua, et al. Sliding Window Change Point Detection Based Dynamic Network Model Inference Framework for Airport Ground Service Process[J]. Knowledge-Based Systems, 2022, 238: 107701.
2	Zheng Yang. A Petri Net Framework for the Representation and Analysis of Aircraft Turnaround Operations[D]. Cambridge: Massachusetts Institute of Technology, 2019.
3	Wu C L, Caves R E. Modelling of Aircraft Rotation in a Multiple Airport Environment[J]. Transportation Research Part E: Logistics and Transportation Review, 2002, 38(3/4): 265-277.
4	Tian Yong, Wan Lili, Ye Bojia, et al. Research on Evaluation of Airport Environment Capacity[J]. Journal of Intelligent & Fuzzy Systems, 2019, 37(2): 1695-1706.
5	Zhang Shuqin, Jiang Wei, Yang Ning, et al. Time Elasticity Evaluation of Flight Turnaround Ground Service Process[C]//Proceedings of the 2021 9th International Conference on Information Technology: IoT and Smart City. New York: ACM, 2021: 519-524.
6	李彪, 王立文, 邢志伟, 等. 过站航班地面保障流程效能评估[J]. 系统工程与电子技术, 2020, 42(7): 1543-1549.
	Li Biao, Wang Liwen, Xing Zhiwei, et al. Effectiveness Evaluation of Ground Support Process for Transit Flight[J]. Systems Engineering and Electronics, 2020, 42(7): 1543-1549.
7	翟文鹏, 陈梵驿, 金嗣博. 基于分位数回归的机场容量评估[J]. 飞行力学, 2016, 34(4): 86-89.
	Zhai Wenpeng, Chen Fanyi, Jin Sibo. Evaluation of the Airport Capacity Based on Quantile Regression[J]. Flight Dynamics, 2016, 34(4): 86-89.
8	Gui Guan, Liu Fan, Sun Jinlong, et al. Flight Delay Prediction Based on Aviation Big Data and Machine Learning[J]. IEEE Transactions on Vehicular Technology, 2020, 69(1): 140-150.
9	Esmaeilzadeh E, Mokhtarimousavi S. Machine Learning Approach for Flight Departure Delay Prediction and Analysis[J]. Transportation Research Record, 2020, 2674(8): 145-159.
10	王立文, 李彪, 邢志伟, 等. 过站航班地面保障过程动态预测[J]. 北京航空航天大学学报, 2021, 47(6): 1095-1104.
	Wang Liwen, Li Biao, Xing Zhiwei, et al. Dynamic Prediction of Ground Support Process for Transit Flight[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(6): 1095-1104.
11	冯霞, 任子云. 基于遗传算法的加油车和摆渡车协同调度研究[J]. 交通运输系统工程与信息, 2016, 16(2): 155-163.
12	Han Xue, Zhao Peixin, Kong Dexin. Two-stage Optimization of Airport Ferry Service Delay Considering Flight Uncertainty[J]. European Journal of Operational Research, 2023, 307(3): 1103-1116.
	Feng Xia, Ren Ziyun. Collaborative Scheduling of Fuelling Vehicle and Ferry Vehicle Based on Genetic Algorithm[J]. Journal of Transportation Systems Engineering and Information Technology, 2016, 16(2): 155-163.
13	许晨晨, 邵荃. 不确定作业时间下机场地面服务保障设备调度优化[J]. 科学技术与工程, 2018, 18(3): 372-378.
	Xu Chenchen, Shao Quan. Optimization of Airport Ground Service Support Equipment Scheduling Under Uncertain Operation Time[J]. Science Technology and Engineering, 2018, 18(3): 372-378.
14	Zheng Hongxing, Liu Xin, Wu Junhui, et al. The On-orbit Mission Analysis of OTV Based on DoDAF[J]. Aircraft Engineering and Aerospace Technology, 2021, 93(6): 937-945.
15	Han Feng, Wang Jianxin, Xiao Gang, et al. Description and Analysis Methods of Information System Architecture Based on DODAF[C]//2008 International Symposium on Information Science and Engineering. Piscataway: IEEE, 2008: 637-642.
16	Gregory J, Berthoud L, Tryfonas T, et al. The Long and Winding Road: MBSE Adoption for Functional Avionics of Spacecraft[J]. Journal of Systems and Software, 2020, 160: 110453.
17	Gough K M, Phojanamongkolkij N. Employing Model-based Systems Engineering (MBSE) on a NASA Aeronautic Research Project: A Case Study[C]//2018 Aviation Technology, Integration, and Operations Conference. Reston: AIAA, 2018: AIAA 2018-3361.
18	Office of the Assistant Secretary of Defense (Acquisition) Washington, DC. Unmanned Systems Integrated Roadmap 2017-2042[EB/OL]. [2023-01-05]. .
19	Cai Chang, Chen Jianfeng, Lei Juan. A System Modeling Method of AUV Swarms Based on UPDM[C]//2019 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC). Piscataway: IEEE, 2019: 1-4.
20	Zhan Zhijuan, Wang Yunhui, Li Bingfei, et al. Architecture Design of Air-sea Joint Combat System Based on DoDAF[C]//AOPC 2019: AI in Optics and Photonics. Bellingham: SPIE, 2019: 113420L.
21	胡晓义, 王如平, 王鑫, 等. 基于模型的复杂系统安全性和可靠性分析技术发展综述[J]. 航空学报, 2020, 41(6): 140-151.
	Hu Xiaoyi, Wang Ruping, Wang Xin, et al. Recent Development of Safety and Reliability Analysis Technology for Model-based Complex System[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(6): 140-151.
22	李文屏, 白鹤峰, 赵毅, 等. 基于MBSE的卫星通信系统建模与仿真[J]. 天地一体化信息网络, 2021, 2(1): 69-74, 80.
	Li Wenbing, Bai Hefeng, Zhao Yi, et al. Modeling and Simulation of Satellite Communication System Base on MBSE[J]. Space-Integrated-Ground Information Networks, 2021, 2(1): 69-74, 80.
23	焦洪臣, 雷勇, 张宏宇, 等. 基于MBSE的航天器系统建模分析与设计研制方法探索[J]. 系统工程与电子技术, 2021, 43(9): 2516-2525.
	Jiao Hongchen, Lei Yong, Zhang Hongyu, et al. Research on Modeling and Design Method of Spacecraft System Based on MBSE[J]. Systems Engineering and Electronics, 2021, 43(9): 2516-2525.
24	范秋岑, 毕文豪, 张安, 等. 民用飞机高度控制系统MBSE建模方法[J]. 系统工程与电子技术, 2022, 44(1): 164-171.
	Fan Qiucen, Bi Wenhao, Zhang An, et al. MBSE Modeling Method of Civil Aircraft Altitude Control System[J]. Systems Engineering and Electronics, 2022, 44(1): 164-171.
25	赵新路, 陈雪. 基于效用函数的无人机群分布式协同侦察方法研究[J]. 中国电子科学研究院学报, 2022, 17(6): 577-581.
	Zhao Xinlu, Chen Xue. Research on Distributed Cooperative Reconnaissance Method of UAV Swarm Based on Utility Function[J]. Journal of China Academy of Electronics and Information Technology, 2022, 17(6): 577-581.

字段	属性
进港航班	MU5107
出港航班	MU5116
执行日期	2021-10-08
任务	正班
机型	A333
机位	221

保障节点	时刻
上轮档	13:00
廊桥靠接	13:02
客舱门开	13:03
货舱门开	13:04
下客开始	13:05
下客结束	13:20
配餐开始	13:30
配餐结束	13:51
加油开始	13:42
加油结束	14:05
清洁开始	13:29
清洁结束	13:45
上客开始	14:30
上客结束	14:42
关客舱门	14:44
关货舱门	14:35
撤轮档	14:49

类别	客座数	常见机型
1	150座以下	A319、B733等
2	151~250座	A320、A321、B738等
3	251座以上	A359、A333、B777等

方法	MAE/min	RMSE/min
实际运行	8.71	9.67
决策后运行	7.29	8.72

机型	实际保障时间/min	决策后保障时间/min	计划保障时间/min	决策前相对误差/%	决策后相对误差/%
A319	81	66.78	46	76	45
A321	92	82.54	58	59	42
A333	135	100.85	81	67	25