Dynamic policy for idling time preservation

This study aims to determine and evaluate dynamic idling policies where an agent can idle while some customers remain waiting. This type of policies can be employed in situations where the flow of urgent customers does not allow the agent to spend sufficient time on back-office tasks. We model the system as a single-agent exponential queue with abandonment. The objective is to minimize the system's congestion while ensuring a certain proportion of idling time for the agent. Using a Markov decision process approach, we prove that the optimal policy is a threshold policy according to which the agent should idle above (below) a certain threshold on the queue length if the congestion-related performance measure is concave (convex) with respect to the number of customers present. We subsequently obtain the stationary probabilities, performance measures, and idling time duration, expressed using complex integrals. We show how these integrals can be numerically computed and provide simpler expressions for fast-agent and heavy-traffic asymptotic cases. In practice, the most common way to regulate congestion is to control access to the service by rejecting some customers upon arrival. Our analysis reveals that idling policies allow high levels of idling probability that such rejection policies cannot reach. Furthermore, the greatest benefit of implementing an optimal idling policy occurs when the objective occupation rate is close to 50% in highly congested situations.     

Autonomous maneuver decision-making for a UCAV in short-range aerial combat based on an MS-DDQN algorithm

To solve the problem of realizing autonomous aerial combat decision-making for unmanned combat aerial vehicles (UCAVs) rapidly and accurately in an uncertain environment, this paper proposes a decision-making method based on an improved deep reinforcement learning (DRL) algorithm: the multi-step double deep Q-network (MS-DDQN) algorithm. First, a six-degree-of-freedom UCAV model based on an aircraft control system is established on a simulation platform, and the situation assessment functions of the UCAV and its target are established by considering their angles, altitudes, environments, missile attack performances, and UCAV performance. By controlling the flight path angle, roll angle, and flight velocity, 27 common basic actions are designed. On this basis, aiming to overcome the defects of traditional DRL in terms of training speed and convergence speed, the improved MS-DDQN method is introduced to incorporate the final return value into the previous steps. Finally, the pre-training learning model is used as the starting point for the second learning model to simulate the UCAV aerial combat decision-making process based on the basic training method, which helps to shorten the training time and improve the learning efficiency. The improved DRL algorithm significantly accelerates the training speed and estimates the target value more accurately during training, and it can be applied to aerial combat decision-making.     

基于MAS的武器装备供应管理支持系统设计

对武器装备供应管理系统结构和功能进行了分析,给出系统的一般构成模式和运行要求,阐明了其所需的决策支持;以系统一般模式为背景,探讨了利用多Agent系统理论构建装备供应管理支持系统的思路,提出支持系统的组成框架,并说明了其主要特点;最后,对支持系统的信息监控过程做了具体研究,给出了相应程序。     

基于灰局势决策理论的维修级别分析方法

简要介绍了某型无人机维修级别分析及灰局势决策理论的一般概念,针对传统维修级别分析中经济性分析的数据来源广、模型可信度较差的情况,研究基于灰局势决策原理建模的一般方法,通过筛选原始数据并修正计算公式,大大减少了冗余计算,最后运用这一模型对某机载无线电设备进行了经济性分析。     

基于半马氏决策过程的电容器维修策略优化

针对电容器随机劣化失效的特点,采用半马氏决策过程解决其预防性维修方案设计和维修策略优化问题.在电容器随机劣化的Gaussian-Poisson模型基础上,以检测周期为优化变量,同时考虑预防性维修次数阈值的影响,建立电容器长期运行费用率优化模型.分析表明,对该型号电容器来说,预防性维修相比于事后修理更加有利于节省部件长期运行的费用率.     

多平台侦察水雷优化模型

针对侦察水雷作战特点,运用最优化理论解决多平台侦察水雷问题。建立多平台侦察水雷优化模型,并通过基于效能指标的单类平台侦察水雷作战模型和基于二分法的最大侦察搜索区域宽度解算模型,得到各类平台的数量需求及其最佳侦察线路配置等关键参数的最优解,较好地满足了侦察水雷作战需求。     

自行火炮状态维修决策支持系统

针对现阶段自行火炮定期维修中存在问题,为了预防自行火炮的故障以及缩短维修时间,提高自行火炮维修的针对性和有效性,在自行火炮维修中引入了基于状态的维修(Condition Based Maintenance,CBM),从工程应用的角度出发,设计并开发了自行火炮状态维修决策支持系统(CBMDSS),明确了系统设计的目标,构建了系统的总体结构,详细阐述了系统实现的具体功能,应用Visual Studio.Net及Matlab相结合的程序开发语言开发了自行火炮状态维修决策支持系统,对自行火炮开展状态维修具有十分重要的使用价值。     

导弹目标识别的最小贝叶斯风险分类器

在贝叶斯决策理论的基础上,通过划分目标特性空间的方法,推导了目标分类问题的总风险表达式.并根据导弹目标识别的特点,建立了导弹目标识别的最小贝叶斯风险模型,给出了基于风险分析的导弹目标识别的具体实现方法.最后,依据导弹目标的实际情况给出了仿真参数并进行了仿真试验,试验结果证明了本方法的有效性.     

战场环境下无人车辆战术机动路径规划方法

针对不同类型威胁体存在的战场环境中无人车辆战术机动路径规划问题,提出了一种基于威胁代价地图的粒子群优化（Particle Swarm Optimization,PSO）方法。借助极坐标系中关键点的极角进行路径描述,并使用分段3次Hermite插值方法形成光滑路径,将路径规划问题转化为关键点极角的参数优化问题。针对基本PSO（BPSO）算法存在的早熟收敛和后期迭代效率低的缺陷,借鉴以群集方式生活的物种按照不同任务对种群进行分工的机制,提出了一种基于多任务子群协同的改进粒子群优化（Particle Swarm Optimization based on the Multi—tasking Subpopu—lation Cooperation,PSO-MSC）算法。借助该算法的快速收敛和全局寻优特性实现了最优路径规划。实验结果表明：该算法可以快速有效地实现战场环境下无人车辆的战术机动路径规划,且规划路径安全、平滑。     

假目标配置数量模型及其效费比分析

在现代战争中,示假伪装的地位越来越重要。引入了真假目标的发现识别概率的指数分布模型,综合考虑作战时间、真假目标价格比等因素,确定战场生存能力和配置假目标成本两个目标函数,并以最小加权偏差法确定两者权重,建立基于多目标决策的假目标配置数量模型。最后结合实际问题,将各参数条件带入模型计算出假目标的最佳配置数量,同时利用假目标的效费比模型进行检验分析,从而为战时假目标配置数量的确定提供参考依据。