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

Abstract

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

CLC Number:

E 835.8

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.

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Research on the scheduling problem of ammunition support operations for carrier-based aircraft"

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

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