舰载机弹药保障作业规划方法研究综述

doi:10.12305/j.issn.1001-506X.2026.04.19

系统工程与电子技术 ›› 2026, Vol. 48 ›› Issue (4): 1303-1321.doi: 10.12305/j.issn.1001-506X.2026.04.19

• 系统工程 • 上一篇

舰载机弹药保障作业规划方法研究综述

张少辉¹^,²(), 李璐璐¹^,², 李亚飞¹^,²^,³, 吴庆顺¹^,², 李冠峰⁴, 徐明亮¹^,²^,³^,*

1. 郑州大学计算机与人工智能学院，河南郑州 450001
2. 智能集群系统教育部工程研究中心，河南郑州 450001
3. 国家超级计算郑州中心，河南郑州 450001
4. 中国船舶集团有限公司第七一三研究所，河南郑州 450015

收稿日期:2024-12-11 修回日期:2025-06-05 出版日期:2026-01-13 发布日期:2026-01-13
通讯作者: 徐明亮 E-mail:zhangsh@zknu.edu.cn
作者简介:张少辉（1982—），男，副教授，博士研究生，主要研究方向为航保作业规划调度、人机融合智能系统
李璐璐（1997—），女，助理研究员，博士，主要研究方向为集群智能计算、协同决策
李亚飞（1983—），男，教授，博士，主要研究方向为空间众包、集群智能
吴庆顺（1995—），男，博士研究生，主要研究方向为集群智能、舰载航空保障技术
李冠峰（1974—），男，研究员，博士，主要研究方向为舰载航空规划与调度
基金资助:
国家自然科学基金杰出青年科学基金(62325602); 国家自然科学基金重点项目(62036010); 国家自然科学基金面上项目(62372416, 62172457); 河南省自然科学基金重点项目(242300421215); 河南省科技攻关项目(262102211064); 河南省高等学校重点科研项目(25B520021)资助课题

Research on the planning methods of ammunition support operations for carrier-based aircraft: a survey

Shaohui ZHANG¹^,²(), Lulu LI¹^,², Yafei LI¹^,²^,³, Qingshun WU¹^,², Guanfeng LI⁴, Mingliang XU¹^,²^,³^,*

1. School of Computer Science and Artificial Intelligence，Zhengzhou University，Zhengzhou 450001，China
2. Intelligent Cluster System Engineering Research Center of Ministry of Education，Zhengzhou 450001，China
3. National Supercomputing Center in Zhengzhou，Zhengzhou 450001，China
4. The 713 Research Institute，China Shipbuilding Industry Corporation，Zhengzhou 450015，China

Received:2024-12-11 Revised:2025-06-05 Online:2026-01-13 Published:2026-01-13
Contact: Mingliang XU E-mail:zhangsh@zknu.edu.cn

摘要/Abstract

摘要：

舰载机弹药保障作业规划是一类典型的复杂系统优化问题，涉及时间、空间、资源等多维约束条件下“人-机-车-物”等要素的协同调度，具有高动态、强实时、非完备、紧耦合等显著特征，当前该领域研究面临着规划方法适应性不强、保障态势感知能力不足、人机协同调度智能化水平不高等挑战。深入剖析了舰载机弹药保障作业的典型作业流程及特征，系统阐述了国内外研究进展与前沿动态，明确了当前的研究热点并展望了未来的发展趋势。首先，分析了舰载机弹药保障作业的作业流程及其耦合影响因素，基于动态环境下的多重约束条件，构建了适用于该问题的混合整数规划模型，并采用马尔可夫决策过程进行形式化描述；其次，从最优化方法、仿真建模、群体智能优化和基于学习的方法等多元视角，总结了现有国内外研究在解决舰载机弹药保障作业规划问题中的适用性与局限性；最后，针对当前研究挑战，提出了若干具有理论创新价值和工程应用前景的研究方向，为进一步提升舰载机弹药保障作业的智能化水平提供理论支撑和实践参考。

关键词: 舰载机弹药保障作业, 作业规划, 调度优化, 强化学习, 人机协同决策, 仿真推演

Abstract:

The planning of ammunition support operations for carrier-based aircraft is a typical complex “human-aircraft-vehicle-resource” optimization problem, influenced by multiple constraints such as time, space, and resources. It is characterized by high dynamism, strong real-time requirements, incompleteness, and tight coupling, and current research in this field faces challenges including poor adaptability of planning methods, insufficient situational awareness, and limited intelligence in human-machine collaborative scheduling. This paper provides an in-depth analysis of the typical operational processes and characteristics of ammunition support operations for carrier-based aircraft, systematically reviews domestic and international research progress and frontier dynamics, identifies current research priorities, and forecasts future development trends. Firstly, the operational workflow and coupling factors of ammunition support operations for carrier-based aircraft are analyzed. Based on multiple constraints in dynamic environments, a mixed-integer programming model suitable for this problem is constructed and formalized using a markov decision process. Secondly, from the diverse perspectives of optimization methods, simulation modeling, swarm intelligence optimization, and learning-based approaches, the applicability and limitations of existing global research in solving ammunition support operations for carrier-based aircraft planning problems are summarized. Finally, addressing contemporary challenges, several research directions with theoretical innovation value and engineering application prospects are proposed to provide theoretical support and practical references for further enhancing the intelligence level of ammunition support operations for carrier-based aircraft.

Key words: ammunition support operations for carrier-based aircraft, operational planning, scheduling optimization, reinforcement learning, human-machine collaborative decision-making, simulation and deduction

中图分类号:

E 835.8

张少辉, 李璐璐, 李亚飞, 吴庆顺, 李冠峰, 徐明亮. 舰载机弹药保障作业规划方法研究综述[J]. 系统工程与电子技术, 2026, 48(4): 1303-1321.

Shaohui ZHANG, Lulu LI, Yafei LI, Qingshun WU, Guanfeng LI, Mingliang XU. Research on the planning methods of ammunition support operations for carrier-based aircraft: a survey[J]. Systems Engineering and Electronics, 2026, 48(4): 1303-1321.

图/表 11

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舰载机弹药保障作业调度相关问题研究"

方法类别	优缺点	问题模型及求解算法	应用场景	代表文献
最优化方法	优点：能够求出精确最优解，容易理解和实现缺点：需要问题精确模型，难以求解大规模问题	网络计划技术	舰载机弹药转运调度优化	[28]
		线性规划	舰载机航空保障资源调度优化	[29−30]
		多目标规划	考虑优先级的单类型舰载机弹药转运	[31]
		可拓理论	舰载机弹药保障作业流程优化及能效评估	[32−33]
仿真建模方法	优点：能有效辅助复杂系统行为分析与理解，实现复杂系统的建模、仿真、推演和评估缺点：易受参数输入不确定性的限制，影响结果的准确性	系统动力学	舰载机弹药贮运系统建模与仿真	[34]
		系统效能评估模型	弹药保障系统效能评估	[35]
		博弈理论	弹药调度策略多目标优化	[36−37]
		Unity3D虚拟仿真	融入虚拟交互的弹药调度保障	[38]
		模糊优化理论	舰载机弹药保障不确定性系统建模	[39]
		多智能体系统	舰载机弹药保障调度路径规划	[40]
群体智能优化方法	优点：无需先验知识，可对解空间进行大范围搜索，全局优化能力强；能够处理复杂非线性问题和大规模问题缺点：求解时间较长，时效性不强；无法存储和利用历史经验；算法性能不够稳定	多资源约束调度模型	不确定性环境下舰载机动态调度	[41]
		线性规划，GA/PSO/ ACO等	舰载机弹药保障调度优化	[42−49]
		禁忌搜索GA	含干扰事件的多机保障及重调度	[50]
		混合整数规划，免疫算法	兼顾挂载/回收的弹药保障作业规划	[51]
		混合整数规划，NSGA-Ⅱ	舰载机弹药挂载作业调度	[52]
		自适应人工蜂群算法	舰载机任务分配/弹药配置协同优化	[53]
		混合优化算法	舰载机弹药保障调度优化	[54−55]
基于学习的方法	优点：具有持续学习和进化能力,通用性好、执行效率高缺点：大模型训练依赖强大算力,解空间维数灾难，模型可解释性差	MDP，策略迭代算法	分布式弹药保障动态重规划	[56]
		改进Transformer	对抗环境下航空弹药目标分配	[57]
		MDP，DQN/QMIX	航保作业实时调度及人机协同决策	[3,23,58]
		MDP，A2C算法	舰载机航保作业两阶段动态规划	[59]

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参考文献 99

1	姜龙光. 国外航母航空保障系统[M]. 北京: 国防工业出版社, 2016.
	JIANG L G. Foreign aircraft carrier aviation support system[M]. Beijing: National Defence Industry Press, 2016.
2	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): 230- 247. doi: 10.7527/S1000-6893.2023.28485
	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): 230- 247. doi: 10.7527/S1000-6893.2023.28485
3	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.
4	LIU J, HAN W, WANG X W, et al. Research on cooperative trajectory planning and tracking problem for multiple carrier aircraft on the deck[J]. IEEE System Journal, 2020, 14 (2): 3027- 3038. doi: 10.1109/JSYST.2019.2932783
5	WU Y, SUN L G, QU X J. A sequencing model for a team of aircraft landing on the carrier[J]. Aerospace Science and Technology, 2016, 54 (7): 72- 87. doi: 10.1016/j.ast.2016.04.007
6	LIU Y J, HAN W, SU X C, et al. Optimization of fixed aviation support resource station configuration for aircraft carrier based on aircraft dispatch mission scheduling[J]. Chinese Journal of Aeronautics, 2023, 36 (2): 127- 138. doi: 10.1016/j.cja.2022.06.023
7	苏析超, 韩维, 张勇, 等. 考虑人机匹配模式的舰载机甲板机务勤务保障调度算法[J]. 航空学报, 2018, 39 (12): 221- 239.
	SU X C, HAN W, ZHANG Y, et al. Scheluling algorithm for maintenance and service support of carrier-based aircraft on flight deck with different man-aircraft matching patterns[J]. Acta Aeronautica et Astronautica Sinica, 2018, 39 (12): 221- 239.
8	WANG X W, LIU J, SU X C, et al. A review on carrier aircraft dispatch path planning and control on deck[J]. Chinese Journal of Aeronautics, 2020, 33 (12): 3039- 3057. doi: 10.1016/j.cja.2020.06.020
9	WU Y, WANG Y Y, QU X J, et al. Exploring mission planning method for a team of carrier aircraft launching[J]. Chinese Journal of Aeronautics, 2019, 32 (5): 1256- 1267. doi: 10.1016/j.cja.2018.08.012
10	金钊, 金璐, 张博闻, 等. 舰载机弹药保障作业调度的形式化建模与验证[J]. 软件学报, 2024, 35 (9): 4100- 4122. doi: 10.13328/j.cnki.jos.007128
	JIN Z, JIN L, ZHANG B W, et al. Modeling and verification of carrier-borne aircraft ammunition support operation scheduling[J]. Journal of Software, 2024, 35 (9): 4100- 4122. doi: 10.13328/j.cnki.jos.007128
11	郭漩, 王宁, 于淑彤, 等. 航空母舰多阶段弹药转运序列可视分析[J]. 计算机辅助设计与图形学学报, 2025, 37 (9): 1515- 1525.
	GUO X, WANG N, YU S T, et al. Visual analysis for multi-stage ammunition transfer sequences on aircraft carrier[J]. Journal of Computer-Aided Design & Computer Graphics, 2025, 37 (9): 1515- 1525.
12	UZUN B. A multimodal ouija board for aircraft carrier deck operations[D]. Cambridge: Massachusetts Institute of Technology, 2016.
13	AVERS K, JOHNSON B, BANKS J, et al. Technical documentation challenges in aviation maintenance: a proceedings report[R]. Washington D C: The U. S. Federal Aviation Administration, 2012.
14	RYAN J C, BANERJEE A G, CUMMINGS M L, et al. Comparing the performance of expert user heuristics and an integer liner program in aircraft carrier deck operations[J]. IEEE Trans. on Cybernetics, 2014, 44 (6): 761- 773. doi: 10.1109/TCYB.2013.2271694
15	ACQUAH K. Towards a multimodal Ouija board for aircraft carrier deck operations[D]. Cambridge: Massachusetts Institute of Technology, 2015.
16	刘翱, 刘克. 舰载机保障作业调度问题研究进展. 系统工程理论与实践, 2017, 37(1): 49−60.
	LIU A, LIU K. Advance in carrier-based aircraft deck operation scheduling[J]. Systems Engineering-Theory & Practice, 2017, 37(1): 49−60.
17	卞大鹏, 黄祥钊, 代丽红, 等. 甲板舰载机动态调度研究方法综述[J]. 电子科技, 2016, 29 (1): 169- 172. doi: 10.16180/j.cnki.issn1007-7820.2016.01.045
	BIAN D P, HUANG X Z, DAI L H, et al. Review of research methods for dynamic scheduling of carrier aircraft[J]. Electronic Science and Technology, 2016, 29 (1): 169- 172. doi: 10.16180/j.cnki.issn1007-7820.2016.01.045
18	张梦钰, 豆亚杰, 陈子夷, 等. 深度强化学习及其在军事领域中的应用综述[J]. 系统工程与电子技术, 2024, 46 (4): 1297- 1308.
	ZHANG M Y, DOU Y J, CHEN Z Y, et al. Deep reinforcement learning and its applications in military field[J]. Systems Engineering and Electronics, 2024, 46 (4): 1297- 1308.
19	史文强, 李彦庆, 陈练. 航母的航空弹药贮运作业解析[J]. 舰船科学技术, 2013, 35 (6): 136- 141. doi: 10.3404/j.issn.1672-7649.2013.06.030
	SHI W Q, LI Y Q, CHEN L. Analysis for the ordnance handling process aboard aircraft carrier[J]. Ship Science and Technology, 2013, 35 (6): 136- 141. doi: 10.3404/j.issn.1672-7649.2013.06.030
20	王能建, 刘钦辉, 李江, 等. 舰载机出动回收能力仿真研究[M]. 北京: 科学出版社, 2018.
	WANG N J, LIU Q H, LI J, et al. Simulation on aircraft sortie generation rate[M]. Beijing: Science Press, 2018.
21	U. S Department of the Navy, Naval Air systems Command. Aircraft operating procedures for air-capable ships NATOPS manual: NAVAIR 00-80T-122[R]. Maryland: Department of the U. S. Navy, 2012: 543−623.
22	李璐璐, 朱睿杰, 隋璐瑶, 等. 智能集群系统的强化学习方法综述[J]. 计算机学报, 2023, 46 (12): 2573- 2596.
	LI L L, ZHU R J, SUI L Y, et al. The reinforcement learning methods for intelligent collective system: a survey[J]. Chinese Journal of Computers, 2023, 46 (12): 2573- 2596.
23	白天, 罗永亮, 刘敬, 等. 基于变作业窗深度强化学习的舰面保障动态调度方法[J]. 船舶工程, 2021, 43 (S2): 117- 123.
	BAI T, LUO Y L, LIU J, et al. Dynamic aircraft scheduling method on flight deck based on variable operation window deep reinforcenemnt learning[J]. Ship Engineering, 2021, 43 (S2): 117- 123.
24	ZHENG M, YANG F Q, DONG Z P, et al. Carrier-borne aircrafts aviation operation automated scheduling using multiplicative weights apprenticeship learning[J]. International Journal of Advanced Robotic Systems, 2019, 16, 1- 16. doi: 10.1177/1729881419828917
25	KIM G, SHIN J, KIM G, et al. Control of fab lifters via deep reinforcement learning: a semi-MDP approach[J]. IEEE Trans. on Automation Science and Engineering, 2024, 21 (4): 5136- 5148. doi: 10.1109/TASE.2023.3308849
26	SCHARPFF J, ROIJERS D, OLIEHOEK F, et al. Solving transition-independent multi-agent MDPs with sparse interactions[C]//Proc. of the AAAI Conference on Artificial Intelligence, 2016.
27	HAO H J, ZHANG X Q, CHI Y, et al. Cooperative carrier aircraft support operation scheduling via multi-agent reinforcement learning[C]//Proc. of the 24th IEEE International Conference on Mobile Data Management, 2023: 297−302.
28	陶益, 魏嘉彧, 李海军, 等. 基于网络计划技术的舰上弹药调度流程优化[J]. 舰船电子工程, 2021, 41 (2): 135- 139. doi: 10.3969/j.issn.1672-9730.2021.02.032
	TAO Y, WEI J Y, LI H J, et al. Optimization of ship ammunition scheduling process based on network planning technique[J]. Ship Electronic Engineering, 2021, 41 (2): 135- 139. doi: 10.3969/j.issn.1672-9730.2021.02.032
29	袁泉, 马羚, 吕晓峰. 母舰航空弹药转运流程规划方法[J]. 火力与指挥控制, 2024, 49 (5): 88- 95，101. doi: 10.3969/j.issn.1002-0640.2024.05.012
	YUAN Q, MA L, LV X F. Analyzing methods research on aviation ammunition transportation process planning of aircraft carrier[J]. Fire Control & Command Control, 2024, 49 (5): 88- 95，101. doi: 10.3969/j.issn.1002-0640.2024.05.012
30	谭大力, 王云飞, 于连飞, 等. 基于整数线性规划方法的舰载机航空保障资源优化调度[J]. 中国舰船研究, 2019, 14 (5): 145- 151. doi: 10.19693/j.issn.1673-3185.01493
	TAN D L, WANG Y F, YU L F, et al. Optimal scheduling of aviation support resources for carrier based aircrafts based on integer linear programming[J]. Chinese Journal of Ship Research, 2019, 14 (5): 145- 151. doi: 10.19693/j.issn.1673-3185.01493
31	杨芸, 李彪, 王帅磊. 带优先级的单类型航空弹药转运多目标规划模型[J]. 指挥控制与仿真, 2016, 38 (6): 46- 52. doi: 10.3969/j.issn.1673-3819.2016.06.011
	YANG Y, LI B, WANG S L. Multi-objective programming model to single type of air ammunition transfer with priority level constraints[J]. Command Control & Simulation, 2016, 38 (6): 46- 52. doi: 10.3969/j.issn.1673-3819.2016.06.011
32	王丰, 李瑞鹏. 航母航空弹药保障能力优化的可拓策略生成研究[J]. 兵工自动化, 2023, 42 (6): 8- 11，26.
	WANG F, LI R P. Research on extension strategy generation of aircraft carrier air ammunition support capability optimization[J]. Ordnance Industry Automation, 2023, 42 (6): 8- 11，26.
33	韩维, 李正阳, 苏析超. 基于改进ANP和可拓理论的航空保障系统效能评估[J]. 兵器装备工程学报, 2019, 40 (8): 100- 105，197. doi: 10.11809/bqzbgcxb2019.08.021
	HAN W, LI Z Y, SU X C. Effectiveness evaluation of carrier aviation support system based on improved ANP and extension theory[J]. Journal of Ordnance Equipment Engineering, 2019, 40 (8): 100- 105，197. doi: 10.11809/bqzbgcxb2019.08.021
34	岳奎志, 韩维, 陈小卫, 等. 载机军舰航空弹药贮运系统建模与仿真分析[J]. 计算机应用, 2011, 31 (12): 3425- 3428，3433.
	YUE K Z, HAN W, CHEN X W, et al. Modelling and simulation analysis on storage-transportation system of aviation ammunition in aircraft carrier[J]. Journal of Computer Applications, 2011, 31 (12): 3425- 3428，3433.
35	郭小威, 吕晓峰, 马登武. 基于FNP-ADC模型的弹药调度系统保障效能评估[J]. 数学的实践与认识, 2014, 44 (22): 195- 204.
	GUO X W, LV X F, MA D W. Support effectiveness evaluation of ammunition scheduling system based on FNP-ADC model[J]. Mathematics in Practice and Theory, 2014, 44 (22): 195- 204.
36	田德红. 航空弹药供应保障模型及决策支持系统的设计研究[D]. 南京: 东南大学, 2018.
	TIAN D H. Research on spply support model and decision support system design of aviation ammunition[D]. Nanjing: Southeast University, 2018.
37	侯德飞, 田德红, 林聪仁, 等. 基于博弈的多目标弹药调度策略优化研究[J]. 南京航空航天大学学报, 2019, 51 (6): 841- 847.
	HOU D F, TIAN D H, LIN C R, et al. Optimization of multi-objective ammunition scheduling strategies based on game theory[J]. Journal of Nanjing University of Aeronautics and Astronautics, 2019, 51 (6): 841- 847.
38	刘哲, 陈佳峰, 马俊飞, 等. 舰载机弹药保障调度仿真系统[J]. 系统仿真学报, 2024, 36 (7): 1621- 1630. doi: 10.16182/j.issn1004731x.joss.23-0393
	LIU Z, CHEN J F, MA J F, et al. Simulation system for carrier-based aircraft ammunition support scheduling[J]. Journal of System Simulation, 2024, 36 (7): 1621- 1630. doi: 10.16182/j.issn1004731x.joss.23-0393
39	夏国清, 栾添添, 孙明晓, 等. 舰载机弹药调运不确定系统的T-S模糊优化模型[J]. 控制与决策, 2018, 33 (4): 639- 643. doi: 10.13195/j.kzyjc.2017.0271
	XIA G Q, LUAN T T, SUN M X, et al. T-S fuzzy optimization model for uncertain weapons transporting system in carrier aircraft[J]. Control and Decision, 2018, 33 (4): 639- 643. doi: 10.13195/j.kzyjc.2017.0271
40	田德红, 何建敏, 齐洁, 等. 航空弹药动态调运决策优化建模与仿真研究[J]. 西北工业大学学报, 2018, 36 (6): 1236- 1242. doi: 10.3969/j.issn.1000-2758.2018.06.028
	TIAN D H, HE J M, QI J, et al. Research on the modeling and simulation of optimal dynamic aerial ammunition scheduling and transportation[J]. Journal of Northwestern Polytechnical University, 2018, 36 (6): 1236- 1242. doi: 10.3969/j.issn.1000-2758.2018.06.028
41	YUAN P L, HAN W, SU X C, et al. A dynamic scheduling method for carrier aircraft support operation under uncertain conditions based on rolling horizon strategy[J]. Applied Sciences, 2018, 8 (9): 1546. doi: 10.3390/app8091546
42	张洪亮, 刘建伟, 马羚, 等. 基于离散粒子群的舰载机弹药调度[J]. 舰船电子工程, 2021, 41 (4): 146- 149. doi: 10.3969/j.issn.1672-9730.2021.04.032
	ZHANG H L, LIU J W, MA L, et al. Ammunition scheduling of carrier based aircraft based on discrete particle swarm pptimization[J]. Ship Electronic Engineering, 2021, 41 (4): 146- 149. doi: 10.3969/j.issn.1672-9730.2021.04.032
43	WANG L T, LI F Q, HUANG J R, et al. Optimization design of ammunition scheduling scheme for carrier-based aircraft based on improved DPSO algorithm[C]//Proc. of the 5th International Conference on Algorithms, Computing and Artificial Intelligence, 2022.
44	LIU M, LI G F. Ammunition scheduling method in airborne weapon depot based on improved genetic algorithm[J]. Journal of Physics: Conference Series, 2021, 1948 (1): 012050. doi: 10.1088/1742-6596/1948/1/012050
45	YUAN Q, WANG L T, ZHENG X M, et al. Ammunition scheduling of shipboard aircraft according to improved ant colony algorithm[C]//Proc. of the 5th International Conference on Algorithms, Computing and Artificial Intelligence, 2022: 95.
46	吕晓峰, 杨东泽, 马羚. 舰载机模块化弹药调度方案优化设计[J]. 系统工程与电子技术, 2023, 45(2): 465−471.
	LYU X F, YANG D Z, MA L. Optimal design of modular ammunition scheduling scheme for carrier-based aircraft[J]. Systems Engineering and Electronics, 2023, 45(2): 465−471.
47	陶俊权, 苏析超, 韩维, 等. 基于EDA算法的航母弹药调度优化研究[J]. 兵器装备工程学报, 2022, 43 (5): 125- 131. doi: 10.11809/bqzbgcxb2022.05.021
	TAO J Q, SU X C, HAN W, et al. Study of aircraft carrier ammunition scheduling optimization based on EDA algorithm[J]. Journal of Ordnance Equipment Engineering, 2022, 43 (5): 125- 131. doi: 10.11809/bqzbgcxb2022.05.021
48	陈涛, 王栋. 基于遗传算法的机载武器调度优化[J]. 海军航空工程学院学报, 2016, 31 (1): 58- 62. doi: 10.7682/j.issn.1673-1522.2016.01.011
	CHEN T, WANG D. Scheduling optimization of aircraft weapons based on genetic algorithm[J]. Journal of Naval Aeronautical and Astronautical University, 2016, 31 (1): 58- 62. doi: 10.7682/j.issn.1673-1522.2016.01.011
49	吕晓峰, 杨东泽, 刘瑜, 等. 舰载机弹药调度演示系统设计与实现[J]. 舰船电子工程, 2023, 43 (6): 121- 125. doi: 10.3969/j.issn.1672-9730.2023.06.026
	LYU X F, YANG D Z, LIU Y, et al. Design and implementation of carrier-based aircraft ammunition scheduling demonstration system[J]. Ship Electronic Engineering, 2023, 43 (6): 121- 125. doi: 10.3969/j.issn.1672-9730.2023.06.026
50	刘珏, 王能建, 罗旭, 等. 采用改进遗传算法的舰载机保障调度方法[J]. 国防科技大学学报, 2020, 42 (2): 194- 205. doi: 10.11887/j.cn.202002026
	LIU J, WANG N J, LUO X, et al. Deck operation scheduling method of carrier-based aircraft based on improved genetic algorithm[J]. Journal of National University of Defense Technology, 2020, 42 (2): 194- 205. doi: 10.11887/j.cn.202002026
51	GUO F, HAN W, SU X C, et al. A bi-population immune algorithm for weapon transportation support scheduling problem with pickup and delivery on aircraft carrier deck[J]. Defence Technology, 2023, 22 (4): 119- 134. doi: 10.1016/j.dt.2021.12.006
52	吕晓峰, 杨东泽, 王丽婷, 等. 基于弹药挂载工作的舰载机保障作业调度[J]. 兵器装备工程学报, 2024, 45 (1): 114- 122，165. doi: 10.11809/bqzbgcxb2024.01.015
	LYU X F, YANG D Z, WANG L T, et al. Carrier-based aircraft support operation scheduling based on ammunition loading work[J]. Journal of Ordnance Equipment Engineering, 2024, 45 (1): 114- 122，165. doi: 10.11809/bqzbgcxb2024.01.015
53	郭放, 韩维, 刘洁, 等. 舰载机任务分配与弹药配置协同优化[J]. 兵工学报, 2025, 46 (5): 332- 345. doi: 10.12382/bgxb.2024.0461
	GUO F, HAN W, LIU J, et al. Collaborative optimization of carrier-based aircraft task assignment and ammunition configuration[J]. Acta Armamentarii, 2025, 46 (5): 332- 345. doi: 10.12382/bgxb.2024.0461
54	刘丹阳. 基于改进PSO算法的舰载机弹药保障优化与仿真研究[D]. 哈尔滨: 哈尔滨工程大学, 2023.
	LIU D Y. Research on optimization and simulation of shipboard aircraft ammunition support based on improved PSO algorithm[D]. Harbin: Harbin Engineering University, 2023.
55	刘哲, 马俊飞, 陈佳峰, 等. 基于改进灰狼算法的舰载机弹药保障调度优化[J]. 系统工程与电子技术, 2024, 46 (4): 1264- 1272.
	LIU Z, MA J F, CHEN J F, et al. Carrier-based aircraft ammunition support scheduling optimization based on improved grey wolf optimizer[J]. Systems Engineering and Electronics, 2024, 46 (4): 1264- 1272.
56	曾斌, 樊旭, 李厚朴. 支持重规划的战时保障动态调度研究[J]. 自动化学报, 2023, 49 (7): 1519- 1529. doi: 10.16383/j.aas.c200416
	ZENG B, FAN X, LI H P. Research of dynamic scheduling with re-planning for wartime logistics support[J]. Acta Automatica Sinica, 2023, 49 (7): 1519- 1529. doi: 10.16383/j.aas.c200416
57	肖友刚, 金升成, 毛晓, 等. 基于深度强化学习的舰船导弹目标分配方法[J]. 控制理论与应用, 2024, 41 (6): 990- 998.
	XIAO Y G, JIN S C, MAO X, et al. Missile-target assignment method of naval ship based on deep reinforcement learning[J]. Control Theory & Applications, 2024, 41 (6): 990- 998.
58	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. doi: 10.1360/SSI-2022-0403
	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. doi: 10.1360/SSI-2022-0403
59	黄珈其, 郭宏伟, 杨帅, 等. 航空保障作业两阶段动态调度方法研究[EB/OL]. [2024-11-10]. https://doi.org/10.13700/j.bh.1001-5965.2024.0427.
	HUANG J Q, GUO H W, YANG S, et al. Research on the two-stage dynamic scheduling method for aviation support operations[EB/OL]. [2024-11-10]. https://doi.org/10.13700/j.bh.10 01-5965.2024.0427.
60	HUANG R, WEN W S, ZHOU Z, et al. Dynamic task offloading for multi-UAVs in vehicular edge computing with delay guarantees: a consensus ADMM-based optimization[J]. IEEE Trans. on Mobile Computing, 2024, 23 (12): 13696- 13712. doi: 10.1109/TMC.2024.3437785
61	PARK G, LEE W, LEE K. 3D multi-trajectory and pick-up optimization of UAV for minimizing delivery time with weight restriction[J]. IEEE Trans. on Intelligent Transportation Systems, 2024, 25 (11): 17562- 17573. doi: 10.1109/TITS.2024.3415031
62	WANG X, WANG Y, ZHAO J G, et al. Joint long-term user scheduling and beamforming design for burst IIoT[J]. IEEE Internet of Things Journal, 2024, 11 (12): 22628- 22642. doi: 10.1109/JIOT.2024.3382738
63	LI C C, LV P, MANOCHA D, et al. ACSEE: antagonistic crowd simulation model with emotional contagion and evolutionary game theory[J]. IEEE Trans. on Affective Computing, 2022, 13 (2): 729- 745. doi: 10.1109/TAFFC.2019.2954394
64	CUMMINGS M L. Assessing safety through agent-based simulation for aircraft carrier flight decks[C]//Proc. of the Human Factors and Ergonomics Society Annual Meeting, 2023, 67(1): 1011–1016.
65	MA L. Carrier aircraft support resource procedure optimization based on genetic algorithm[C]//Proc. of the 29th Chinese Control and Decision Conference, 2017: 949−953.
66	周艾捷. 半实物仿真平台中的舰载机及其保障作业调度研究[D]. 武汉: 华中科技大学, 2021.
	ZHOU A J. Research on the scheduling of carrier-based aircraft and its support operation on the semi-physical simulation platform[D]. Wuhan: Huazhong University of Science and Technology, 2021.
67	CUMMINGS M L, LI S, HAN H, et al. Modeling efficiency and safety on an aircraft carrier flight deck[J]. Journal of Defense Modeling and Simulation, 2024, 21 (4): 441- 452. doi: 10.1177/15485129221150939
68	JIANG T T, SU X C, HAN W. Optimization of support scheduling on deck of carrier aircraft based on improved differential evolution algorithm[C]//Proc. of the IEEE International Conference on Control Science and Systems Engineering, 2017: 136−140.
69	ZHENG X M, LI B, WANG L T, et al. Design of carrier ammunition scheduling scheme based on improved genetic algorithm[C] //Proc. of the 7th International Conference on Control Engineering and Artificial Intelligence, 2023: 162−167.
70	袁子龙, 何非, 赵建波, 等. 航母舰载机保障作业任务分配及弹药转运调度优化方法[J]. 兵工学报, 2025, 46 (5): 286- 299.
	YUAN Z L, HE F, ZHAO J B, et al. Optimization method of carrier-borne aircraft support operation assignment and ammunition transfer scheduling[J]. Acta Armamentarii, 2025, 46 (5): 286- 299.
71	SU X C, HAN W, WU Y, et al. A proactive robust scheduling method for aircraft carrier flight deck operations with stochastic durations[J]. Complexity, 2018, 2018: 6932985.
72	WU Y, HU N, QU X. A general trajectory optimization method for aircraft taxiing on flight deck of carrier[J]. Journal of Aerospace Engineering, 2019, 233 (4): 1340- 1353. doi: 10.1177/0954410017752224
73	LU A G, XU X M. Research on the layout method of carrier aircraft on the flight deck of aircraft carriers[C]//Proc. of the International Conference on Man Machine Environment System Engineering, 2024: 300−308.
74	YU L F, ZHU C, LI W J, et al. Research on the aviation support groups scheduling for multi-wave aircrafts based on the ammunition transportation[C]//Proc. of the International Conference on Unmanned Systems, 2021: 383−389.
75	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. doi: 10.1016/j.ast.2020.106346
76	CUI R W, HAN W, SU X C, et al. A dual population multi-operator genetic algorithm for flight deck operations scheduling problem[J]. Journal of Systems Engineering and Electronics, 2021, 32 (2): 331- 346.
77	褚凯轩, 常天庆, 张雷. 基于改进人工蜂群算法的地面作战武器-目标分配[J]. 兵工学报, 2023, 44 (7): 2171- 2183. doi: 10.12382/bgxb.2022.0294
	CHU K X, CHANG T Q, ZHANG L. A ground combat weapon target assignment model based on shooting effectiveness and improved artificial bee colony algorithm[J]. Acta Armamentarii, 2023, 44 (7): 2171- 2183. doi: 10.12382/bgxb.2022.0294
78	FENG Q, BI W, SUN B, et al. Dynamic scheduling of carrier aircraft based on improved ant colony algorithm under disruption and strong constraint[C]//Proc. of the 2nd International Conference on Reliability Systems Engineering, 2017.
79	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. doi: 10.12305/j.issn.1001-506X.2023.10.22
	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. doi: 10.12305/j.issn.1001-506X.2023.10.22
80	LI Y F, WU Q S, HUANG X, et al. Efficient adaptive matching for real-time city express delivery[J]. IEEE Trans. on Knowledge and Data Engineering, 2023, 35 (6): 5767- 5779.
81	ABIODUN O I, JANTAN A, OMOLARA A E, et al. State-of-the-art in artificial neural network applications: a survey[J]. Heliyon, 2018, 4 (11): e00938. doi: 10.1016/j.heliyon.2018.e00938
82	SUTTON R S, BARTO A G. Reinforcement learning: an introduction[M]. Cambridge: MIT Press, 2018.
83	MNIH V, KAVUKCUOGLU K, SILVER D, et al. Human-level control through deep reinforcement learning[J]. Nature, 2015, 518 (7540): 529- 533. doi: 10.1038/nature14236
84	FENG H F, ZENG W. Deep reinforcement learning for carrier-borne aircraft support operation scheduling[C]//Proc. of the International Conference on Intelligent Computing, Automation and Applications, 2021: 929–935.
85	LIU J, HAN W, LI J, et al. Integration design of sortie scheduling for carrier aircrafts based on hybrid flexible flow shop[J]. IEEE Systems Journal, 2019, 14 (1): 1503- 1511. doi: 10.1109/jsyst.2019.2922261
86	LI G F, ZHANG S H, MEI B L, et al. Material transfer planning for huge warship: modeling, simulation, and evaluation[C]//Proc. of the 23rd IEEE International Conference on Mobile Data Management, 2022: 488−493.
87	YU L F, ZHU C, SHI J M, et al. An extended flexible job shop scheduling model for flight deck scheduling with priority, parallel operations, and sequence flexibility[J]. Scientific Programming, 2017, 2017 (1): 2463252. doi: 10.1155/2017/2463252
88	HAN W, GUO F, SU X C. A reinforcement learning method for a hybrid flow-shop scheduling problem[J]. Algorithms, 2019, 12 (11): 222. doi: 10.3390/a12110222
89	LEI K, GUO P, ZHAO W C, et al. A multi-action deep reinforcement learning framework for flexible Job-shop scheduling problem[J]. Expert Systems with Application, 2022, 205, 117796. doi: 10.1016/j.eswa.2022.117796
90	KÄLLSTRÖM J, HEINTZ F. Agent coordination in air combat simulation using multi-agent deep reinforcement learning[C] //Proc. of the IEEE International Conference on Systems, Man, and Cybernetics, 2020: 2157–2164.
91	李凯文, 张涛, 王锐, 等. 基于深度强化学习的组合优化研究进展[J]. 自动化学报, 2021, 47 (11): 2521- 2537. doi: 10.16383/j.aas.c200551
	LI K W, ZHANG T, WANG R, et al. Research reviews of combinatorial optimization methods based on deep reinforcement learning[J]. Acta Automatica Sinica, 2021, 47 (11): 2521- 2537. doi: 10.16383/j.aas.c200551
92	BENGIO Y, LODI A, PROUVOST A. Machine learning for combinatorial optimization: a methodological tour d'horizon[J]. European Journal of Operational Research, 2021, 290 (2): 405- 421. doi: 10.1016/j.ejor.2020.07.063
93	WANG L, MA C, FENG X Y, et al. A survey on large language model based autonomous agents[J]. Frontiers of Computer Science, 2024, 18 (6): 186345. doi: 10.1007/s11704-024-40231-1
94	ZHAO W X, ZHOU K, LI J Y, et al. A survey of large language models[EB/OL]. [2024-10-11]. https//openreview.net/pdf?id-W4tk3qcy2b.
95	薛均晓, 徐明亮, 李亚飞, 等. 面向航空母舰电子显灵板的多智能体建模技术研究进展[J]. 计算机辅助设计与图形学学报, 2021, 33 (10): 1475- 1485. doi: 10.3724/SP.J.1089.2021.18733
	XUE J X, XU M L, LI Y F, et al. Research progress of multi-agent technology for aircraft carrier electronic display panel[J]. Journal of Computer-Aided Design & Computer Graphics, 2021, 33 (10): 1475- 1485. doi: 10.3724/SP.J.1089.2021.18733
96	RYAN J C, CUMMINGS M L. A systems analysis of the introduction of unmanned aircraft into aircraft carrier operations[J]. IEEE Trans. on Human-Machine Systems, 2016, 46 (2): 209- 220. doi: 10.1109/THMS.2014.2376355
97	张韬, 项祺, 郑婉文, 等. 基于改进A*算法的路径规划在海战兵棋推演中的应用[J]. 兵工学报, 2022, 43 (4): 960- 968. doi: 10.12382/bgxb.2021.0209
	ZHANG T, XIANG Q, ZHENG W W, et al. Application of path planning based on improved A* algorithm in war gaming of naval warfare[J]. Acta Armamentarii, 2022, 43 (4): 960- 968. doi: 10.12382/bgxb.2021.0209
98	LIU D W, SUN J, HUANG D G, et al. Research on development status and technology trend of intelligent autonomous ammunition[J]. Journal of Physics Conference Series, 2021, 1721 (1): 012032. doi: 10.1088/1742-6596/1721/1/012032
99	於志文, 孙卓, 程岳, 等. 智能无人机集群协同感知计算研究综述[J]. 航空学报, 2024, 45 (20): 2，7- 22. doi: 10.7527/S1000-6893.2024.30912
	YU Z W, SUN Z, CHENG Y, et al. A review of intelligent UAV swarm collaborative perception and computation[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45 (20): 2，7- 22. doi: 10.7527/S1000-6893.2024.30912

符号	含义	符号	含义
$ I $	波次保障弹药集合，弹药$ i\in I $	$ {O}_{i,j} $	弹药$ i $第$ j $个工序
$ n $	波次保障弹药数量，$ n=\left\| I\right\| $	$ {S}_{i,j} $	弹药$ i $工序$ j $的作业开始时间
$ {J}_{i} $	弹药$ i $的工序集合，工序$ j\in {J}_{i} $	$ {C}_{i,j} $	弹药$ i $工序$ j $的作业完工时间
$ s $	弹药$ i $的工序数量，$ s=\left\| {J}_{i}\right\| $	$ {D}_{i,j,k} $	弹药$ i $工序$ j $在保障设备$ k $上的作业时长
$ {P}_{i,j} $	弹药$ i $工序$ j $的前驱工序集合	$ {x}_{i,j,t} $	决策变量：弹药$ i $工序$ j $在$ t $时刻完成则为1,否则为0
$ {F}_{i,j} $	弹药$ i $工序$ j $的后继工序集合	$ {y}_{i,j,k} $	决策变量：弹药$ i $工序$ j $被分配在设备$ k $上作业则为1，否则为0
$ M $	保障设备集合	$ {\mathrm{dist}}({p}_{x},{p}_{y}) $	两个保障站位之间的距离
$ m $	保障设备数量，$ m=\left\| M\right\| $	$ {C}_{i} $	弹药$ i $保障作业完工时间
$ {M}_{j} $	第$ j $个工序中保障设备集合，设备$ k\in {M}_{j} $	$ T $	最长保障作业时间周期
$ {m}_{j} $	第$ j $个工序中保障设备数量，$ {m}_{j}=\left\| {M}_{j}\right\| $	$ \sigma $	一个可行的波次弹药保障作业调度方案
$ {\overline{w}}_{j} $	第$ j $个工序中保障设备的平均作业负载	$ {C}_{\max }(\sigma ) $	调度方案$ \sigma $的最大保障作业完工时间
$ {p}_{i,j} $	弹药$ i $工序$ j $所需保障设备的位置	$ {\tilde{C}}_{\max } $	限定的波次弹药保障作业截止完工时间
$ {v}_{i} $	装载弹药$ i $后弹药转运车的标准转运速度

类型	决策智能体	状态观测	奖励结构	决策步/时间步	策略更新机制	计算复杂度	适用场景
MDP^[23−24]	单智能体	完全可观测状态	单一全局奖励	单一时间步	基于价值迭代/策略梯度	较低（通常为多项式级别）	单个决策者且环境状态完全可观测，如单架舰载机的维保作业
POMDP^[3]	单智能体	部分可观测状态	单一全局奖励（基于部分观测）	单一时间步，决策依赖于当前观测和行动	基于信念状态的价值函数和策略更新	较高（部分观测增加了计算复杂度）	状态部分可观测且需要推理的场景，如舰载机编队的出动回收作业规划
Semi- MDP^[25]	单或多智能体	完全可观测或部分可观测	单一全局奖励或多个局部奖励	离散决策步，选择的动作会持续一段时间	考虑动作持续时间的价值函数	高（需处理时间不确定性）	作业时间延迟较长的决策过程，如舰载机编队复杂的维保作业调度问题
MMDP^[26]	多智能体	部分可观测状态	每个智能体可能有独立的局部奖励,也可以有全局奖励	每个智能体可能有不同的时间步	考虑智能体间交互的联合策略和个体策略更新	较高（需处理多智能体之间的协调和冲突）	多决策者协作完成任务，如多个保障小组以协作或竞争的形式完成多架次舰载机航保作业任务
Dec- POMDP^[27]	多智能体	部分可观测状态	每个智能体可能有独立的局部奖励	智能体之间信息更新较为复杂，需要处理不同时间尺度和延迟	多智能体信念状态和联合动作空间	极高（部分可观测问题需要复杂推理）	多智能体系统且状态部分可观测的场景，如舰载机大规模弹药保障协同作业

建模方法	适用作业场景	适配规划方法	在弹药保障作业中的典型应用
线性规划	静态确定性保障任务	最优化方法	固定时间窗的弹药挂载序列优化、保障资源分配等作业
线性规划	复杂约束场景	群体智能优化方法	多目标优化、多机型补给协同规划、资源冲突消解等
MDP建模	多因素耦合场景	仿真建模方法	保障作业冲突规避、加油挂弹等多任务协同等作业
MDP建模	动态不确定环境、在线决策	基于学习的方法	弹药保障任务变更、战时应急补给、动态环境序贯决策等

仿真建模工具	技术路线及特点	代表文献
Vensim	结合系统动力学模型方法和因果回路分析，在扰动条件下预测航空弹药需求变化趋势	[34]
Unity3D	基于U3D构建舰载机弹药保障调度三维数字虚拟仿真系统，实现调度建模、优化求解和调度方案可视化，有效提高了弹药保障作业调度效率	[38]
eM-Plant	构建舰载机保障资源流程模型，基于eM-Plant仿真工具和GA工具包优化保障资源利用率，适用于小规模作业时间调度优化问题	[65]
半实物仿真平台	构建了由上位机管理、仿真定位系统、甲板及舰载机仿真实体等模块构成的航母半实物仿真平台，通过无线通信网络实现模块之间的实时数据交互与协同控制。相较于传统数字仿真系统，采用“虚实结合”的半实物仿真平台具有更高的置信度，但在保障作业约束建模、动态事件响应等方面仍存在优化空间，与真实甲板作业需求尚有一定差距	[66]

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舰载机弹药保障作业规划方法研究综述

Research on the planning methods of ammunition support operations for carrier-based aircraft: a survey

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