差异化保障需求驱动的舰载机多机协同决策方法

doi:10.7527/S1000-6893.2024.31274

Abstract

Abstract:

In modern naval warfare， the effectiveness of carrier-borne aircraft operations is crucial， and multi-type carrier-borne aircraft formations have become the fundamental operational paradigm for aircraft carriers. However， collaborative decision making for carrier-borne aircraft support faces significant challenges due to differentiated support processes， deck space limitations， and complex maintenance procedures. To address these scheduling challenges， we propose a novel Dependency-Aware Task Scheduling Decision Module （DATSDM）. By leveraging graph neural networks， DATSDM delves into the intricate network structure of support processes， enabling efficient and precise scheduling of support resources across varying scales and multi-type carrier-borne aircraft clusters. Furthermore， DATSDM incorporates the strengths of transformer， harnessing its attention mechanism to parallelly analyze and process carrier-borne aircraft support information， thereby significantly reducing support times. Extensive experiments demonstrate the remarkable superiority of DATSDM over its peers. In various resource allocation scenarios for multiple carrier-borne aircraft types， DATSDM reduces support time by 13.36%. This improvement significantly enhances the overall support efficiency and combat readiness of multi-type carrier-borne aircraft.

Key words: carrier-based aircraft, deep reinforcement learning, graph neural networks, scheduling optimization, resource allocation

CLC Number:

Wei CHEN, Lulu LI, Dong CHEN, Shaohui ZHANG, Yafei LI, Ke WANG, Yuanyuan JIN, Mingliang XU. Multi-aircraft cooperative decision-making methods driven by differentiated support demands for carrier-based aircraft[J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(13): 531274.

Figures/Tables 14

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Table 1

Resource distribution of support positions

站位	资源	站位	资源
$p 1$	$τ 2, τ 3, τ 8, τ 9$	$p 9$	$τ 1, τ 5, τ 7, τ 11$
$p 2$	$τ 2, τ 4, τ 8, τ 12$	$p 10$	$τ 1, τ 2, τ 4, τ 7, τ 12$
$p 3$	$τ 2, τ 7, τ 11$	$p 11$	$τ 2, τ 3, τ 5, τ 8$
$p 4$	$τ 2, τ 4, τ 6, τ 8, τ 10$	$p 12$	$τ 4, τ 7, τ 8, τ 11$
$p 5$	$τ 1, τ 2, τ 3$	$p 13$	$τ 1, τ 8, τ 12$
$p 6$	$τ 1, τ 2, τ 4, τ 7$	$p 14$	$τ 6, τ 9, τ 10$
$p 7$	$τ 1, τ 9, τ 10$	$p 15$	$τ 2, τ 7, τ 11$
$p 8$	$τ 1, τ 3, τ 8, τ 11$	$p 16$	$τ 3, τ 4, τ 8, τ 9$

Table 1

Table 2

Hyperparameter settings

超参数	数值	超参数	数值
$γ$	0.99	$H$	8
$L 1$	6	$E$	100
$L 2$	6	$E n u m$	1 000
$L 3$	6	$β$	0.001
$α 0$	0.99	$d x$	64

Table 2

Fig.6

Table 3

Comparison of various algorithms in multi-type carrier-borne aircraft scheduling

算法	5 $×$ 2架		10 $×$ 2架		15 $×$ 2架		20 $×$ 2架		25 $×$ 2架		30 $×$ 2架
算法	$ϵ$ /%	Avg/s	$ϵ$ /%	Avg/s	$ϵ$ /%	Avg/s	$ϵ$ /%	Avg/s	$ϵ$ /%	Avg/s	$ϵ$ /%	Avg/s
GREEDY	87.9	1 785.7	102.7	2 195.9	115.9	2 413.3	86.1	2 863.6	69.7	3 319.1	63.7	3 949.4
HAS	77.4	1 855.2	86.4	2 312.2	102.8	2 523.2	75.6	2 932.4	56.9	3 465.9	49.9	4 135.4
MAPPO	73.6	1 716.1	83.6	2 104.7	100.7	2 369.5	376.7	2 768.7	57.8	3 203.9	49.7	3 800.3
MSCTA	80.4	1 720.5	91.6	2 149.8	106.5	2 345.3	81.4	2 790.6	62.3	3 224.1	56.1	3 785.6
DATSDM	73.7	1 719.5	83.3	2 099.4	98.2	2 312.2	68.8	2 659.3	50.3	3 058.5	43.0	3 615.4

Table 3

Fig.7

Table 4

Average waiting time of carrier-borne aircraft with different algorithms

算法	平均等待时间/s
算法	5 $×$ 2架	15 $×$ 2架	25 $×$ 2架
GREEDY	55.44	285.23	513.23
HAS	62.23	275.12	436.65
MAPPO	63.94	299.67	485.54
MSCTA	57.32	330.43	580.48
DATSDM	61.66	276.32	457.23

Table 4

Table 5

Table 6

Fig.8

References 36

[1]	刘东，吴家仁，周一舟，等. 舰载机综合保障技术实践及发展展望［J］. 航空学报， 2021， 42（8）： 525802.
	LIU D， WU J R， ZHOU Y Z， et al. Practice and prospects of comprehensive support technologies of carrier-based aircraft［J］. Acta Aeronautica et Astronautica Sinica， 2021， 42（8）： 525802 （in Chinese）.
[2]	屈也频，金惠明，何肇雄. 航母舰载机装备体系及指标论证方法［J］. 航空学报， 2018， 39（5）： 221675.
	QU Y P， JIN H M， HE Z X. Carrier-based aircraft equipment system-of-systems and index demonstration method‍［J］. Acta Aeronautica et Astronautica Sinica， 2018， 39（5）： 221675 （in Chinese）.
[3]	万兵，韩维，苏析超，等. 基于CE-PF算法的舰载机离场调度优化问题［J］. 北京航空航天大学学报， 2022， 48（5）： 771-785.
	WAN B， HAN W， SU X C， et al. Carrier-based aircraft departure scheduling optimization based on CE-PF algorithm‍［J］. Journal of Beijing University of Aeronautics and Astronautics， 2022， 48（5）： 771-785 （in Chinese）.
[4]	刘翱，刘克. 舰载机保障作业调度问题研究进展［J］. 系统工程理论与实践， 2017， 37（1）： 49-60.
	LIU A， LIU K. Advances in carrier-based aircraft deck operation scheduling［J］. Systems Engineering-Theory & Practice， 2017， 37（1）： 49-60 （in Chinese）.
[5]	万兵，韩维，梁勇，等. 舰载机出动离场调度优化算法［J］. 系统工程与电子技术， 2021， 43（12）： 3624-3634.
	WAN B， HAN W， LIANG Y， et al. Optimization algorithm of carrier-based aircraft sortie departure scheduling［J］. Systems Engineering and Electronics， 2021， 43（12）： 3624-3634 （in Chinese）.
[6]	李亚飞，吴庆顺，徐明亮，等. 基于强化学习的舰载机保障作业实时调度方法［J］. 中国科学：信息科学， 2021， 51（2）： 247-262.
	LI Y F， WU Q S， XU M L， et al. Real-time scheduling for carrier-borne aircraft support operations： A reinforcement learning approach［J］. Scientia Sinica （Informationis）， 2021， 51（2）： 247-262 （in Chinese）.
[7]	朱兴动，孟杨凯，黄葵，等. 基于GA算法的舰载机快速调度［J］. 兵工自动化， 2020， 39（9）： 69-73.
	ZHU X D， MENG Y K， HUANG K， et al. Aircraft fast scheduling based on GA algorithm［J］. Ordnance Industry Automation， 2020， 39（9）： 69-73 （in Chinese）.
[8]	李正阳，张勇，王志梅，等. 基于改进灰色序关系法的舰载机舰面保障方案效能评价［J］. 兵器装备工程学报， 2020， 41（5）： 234-240.
	LI Z Y， ZHANG Y， WANG Z M， et al. Effectiveness evaluation of carrier aircraft support mission scheme based on improved order relation method and grey relation analysis［J］. Journal of Ordnance Equipment Engineering， 2020， 41（5）： 234-240 （in Chinese）.
[9]	吴靳，戴明强，王俊杰，等. 基于学徒制算法的航母舰载机保障作业调度［J］. 中国舰船研究， 2022， 17（4）： 145-154.
	WU J， DAI M Q， WANG J J， et al. Carrier-based aircraft operation support scheduling based on apprenticeship learning agorithm［J］. Chinese Journal of Ship Research， 2022， 17（4）： 145-154 （in Chinese）.
[10]	CUI R W， HAN W， SU X C， et al. A multi-objective hyper heuristic framework for integrated optimization of carrier-based aircraft flight deck operations scheduling and resource configuration‍［J］. Aerospace Science and Technology， 2020， 107： 106346.
[11]	王海峰，王宏亮，阳纯波. 航空装备保障智能化发展认识与探讨［J］. 测控技术， 2020， 39（12）： 1-9， 27.
	WANG H F， WANG H L， YANG C B. Understanding and discussion on intelligence-based aviation materiel support development［J］. Measurement & Control Technology， 2020， 39（12）： 1-9， 27 （in Chinese）.
[12]	王文鹏，邹刚，张玎，等. 基于自适应遗传算法的舰载机保障调度［J］. 兵工自动化， 2021， 40（1）： 37-42.
	WANG W P， ZOU G， ZHANG D， et al. Support scheduling of carrier-based aircraft based on adaptive genetic algorithm［J］. Ordnance Industry Automation， 2021， 40（1）： 37-42 （in Chinese）.
[13]	刘子玄，万兵，苏析超，等. 基于HA算法的舰载机出动作业调度方法［J］. 系统工程与电子技术， 2024， 46（5）： 1691-1702.
	LIU Z X， WAN B， SU X C， et al. Sortie scheduling method of carrier aircraft based on HA algorithm［J］. Systems Engineering and Electronics， 2024， 46（5）： 1691-1702 （in Chinese）.
[14]	范加利，黄葵，朱兴动，等. 基于禁忌算法的舰载机甲板作业动态调度优化算法［J］. 系统工程与电子技术， 2023， 45（10）： 3172-3182.
	FAN J L， HUANG K， ZHU X D， et al. Carrier aircraft deck operations dynamic scheduling optimization algorithm based on the tabu algorithm［J］. Systems Engineering and Electronics， 2023， 45‍（10）： 3172-3182 （in Chinese）.
[15]	LIU J， HAN W， LI J， et al. Integration design of sortie scheduling for carrier aircrafts based on hybrid flexible flowshop‍［J］. IEEE Systems Journal， 2020， 14‍（1）： 1503-1511.
[16]	李亚飞，高磊，蒿宏杰，等. 舰载机保障作业人机协同决策方法［J］. 中国科学：信息科学， 2023， 53（12）： 2493-2510.
	LI Y F， GAO L， HAO H J， et al. Human-machine collaborative decision-making for carrier aircraft support operations［J］. Scientia Sinica （Informationis）， 2023， 53（12）： 2493-2510 （in Chinese）.
[17]	GROVER A， LESKOVEC J. Node2vec： Scalable feature learning for networks［C］∥Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York： ACM， 2016： 855-864.
[18]	MIKOLOV T， CHEN K， CORRADO G， et al. Efficient estimation of word representations in vector space［DB/OL］. arXiv preprint： 1301. 3781， 2013.
[19]	GEISLER S， LI Y， MANKOWITZ D J， et al. Transformers meet directed graphs‍［C］‍∥Proceedings of the 40th International Conference on Machine Learning. New York： ACM， 2023： 11144-11172.
[20]	TOLSTIKHIN I O， HOULSBY N， KOLESNIKOV A， et al. MLP-mixer： An all-MLP architecture for vision［C］∥Proceedings of the 35th International Conference on Neural Information Processing Systems. New York： ACM， 2021： 24261-24272.
[21]	VASWANI A， SHAZEER N， PARMAR N， et al. Attention is all you need［C］∥Proceedings of the 31st International Conference on Neural Information Processing Systems. New York： ACM， 2017： 6000-6010.
[22]	LI Q M， HAN Z C， WU X M. Deeper insights into graph convolutional networks for semi-supervised learning［C］∥Proceedings of the AAAI Conference on Artificial Intelligence. New York： ACM， 2018： 3538-3545.
[23]	VINYALS O， FORTUNATO M， JAITLY N. Pointer networks［C］∥Proceedings of the 29th International Conference on Neural Information Processing Systems. New York： ACM， 2015： 2692-2700.
[24]	CHENG C A， XIE T， JIANG N， et al. Adversarially trained actor critic for offline reinforcement learning［C］∥Proceedings of the 39th International Conference on Machine Learning. 2022： 3852-3878.
[25]	MNIH V， BADIA A P， MIRZA M， et al. Asynchronous methods for deep reinforcement learning‍［C］∥Proceedings of the 33rd International Conference on Machine Learning. 2016： 1928-1937.
[26]	SUTTON R S， MCALLESTER D， SINGH S， et al. Policy gradient methods for reinforcement learning with function approximation［C］∥Proceedings of the 12th International Conference on Neural Information Processing Systems. 1999： 1057-1063.
[27]	WANG Z Y， BAPST V， HEESS N， et al. Sample efficient actor-critic with experience replay‍［DB/OL］. arXiv preprint： 1611. 01224， 2017.
[28]	张少辉，刘舜，李亚飞，等. 航空母舰舰载机弹药保障作业调度优化算法［J］. 航空学报， 2023， 44（20）： 228485.
	ZHANG S H， LIU S， LI Y F， et al. Optimization algorithm for ammunition support operation scheduling of carrier-borne aircraft［J］. Acta Aeronautica et Astronautica Sinica， 2023， 44（20）： 228485 （in Chinese）.
[29]	朱兴动，赵洋，范加利，等. 基于改进引力搜索算法的舰载机一站式保障作业优化调度方法［J］. 舰船电子工程， 2023， 43（8）： 98-103， 196.
	ZHU X D， ZHAO Y， FAN J L， et al. Optimization and scheduling method for shipborne aircraft one-stop support operations based on improved gravity search algorithm［J］. Ship Electronic Engineering， 2023， 43（8）： 98-103， 196 （in Chinese）.
[30]	ZHANG Y N， LI X， TENG Y， et al. A heuristic rule adaptive selection approach for multi-work package project scheduling problem［J］. Expert Systems with Applications， 2024， 238： 122092.
[31]	VAN HASSELT H， GUEZ A， SILVER D. Deep reinforcement learning with double Q-learning［C］∥Proceedings of the 30th AAAI Conference on Artificial Intelligence. New York： ACM， 2016： 2094-2100.
[32]	YU C， VELU A， VINITSKY E， et al. The surprising effectiveness of PPO in cooperative multi-agent games［C］∥Proceedings of the 36th International Conference on Neural Information Processing Systems. 2022： 24611-24624.
[33]	SCHULMAN J， WOLSKI F， DHARIWAL P， et al. Proximal policy optimization algorithms［DB/OL］. arXiv preprint： 1707. 06347， 2017.
	SCHULMAN J， WOLSKI F， DHARIWAL P， et al. Proximal policy optimization algorithms［DB/OL］. arXiv preprint： 1707. 06347， 2017.
[34]	LIU Z， LI K L， ZHOU X， et al. Multi-stage complex task assignment in spatial crowdsourcing［J］. Information Sciences， 2022， 586： 119-139.
[35]	PARK J， CHUN J， KIM S H， et al. Learning to schedule job-shop problems： Representation and policy learning using graph neural network and reinforcement learning［J］. International Journal of Production Research， 2021， 59（11）： 3360-3377.
[36]	KINGMA D P， BA J， HAMMAD M M. Adam： A method for stochastic optimization［DB/OL］. arXiv preprint： 1412. 6980， 2014.

算法	规则
SPT（Shortest Processing Time）	优先选择保障时间最短的舰载机
LPT（Longest Processing Time）	优先选择保障时间最长的舰载机
SWTF（Shortest Wait Time First）	优先选择等待保障时间最短的舰载机
LWTF（Longest Wait Time First）	优先选择等待保障时间最长的舰载机
MTF（Most Tasks First）	优先选择保障作业最多的舰载机
LTF（Least Tasks First）	优先选择保障作业最少的舰载机

算法	规则
SWFT （Shortest Wait Time First）	优先选择等待时间最短的保障站位
LWTF（Longest Wait Time First）	优先选择等待时间最长的保障站位
RANDOM（Random Selection）	随机选择保障站位

Multi-aircraft cooperative decision-making methods driven by differentiated support demands for carrier-based aircraft

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