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.     

RCS特性辅助的CMIMO雷达功率资源分配方法

针对集中式多输入多输出(collocated multiple-input multiple-output, CMIMO)雷达在实际探测过程中,未有效结合目标雷达散射截面(radar cross section, RCS)高动态变化特性导致雷达跟踪精度不高甚至失跟的问题,提出一种基于目标RCS高动态特性的CMIMO雷达功率资源自适应分配方法。考虑到目标RCS特征的角度敏感性,利用目标运动状态的可预测性动态获取实际观测角度,从而完成跟踪帧的极化方式优选;在此基础上构建包含功率与RCS的多目标跟踪误差后验克拉美罗下界,将其作为目标函数进行优化,利用内点惩罚法求解该凸优化问题即可实现RCS高动态情况下的功率优化分配。实验结果表明:该方法可结合目标不同方向RCS动态分集特性实现功率的有效分配,相比于传统RCS模型分配方案,有效解决了分配方案与实际跟踪场景之间的失配问题,从而提升了CMIMO雷达的多目标跟踪性能。     

高性能炸药综合性能的多维评估

基于欧几里得几何距离的概念,给出了一种炸药综合性能的多维空间评估方法。这种方法在确定炸药理想方案和最差方案的基础上,综合考虑了各种炸药方案与理想方案和最差方案的欧几里得几何距离,以此评估炸药综合性能。     

影响指挥员军事决策的因素分析

现代高技术条件下的局部战争,军事活动的节奏明显加快,作战空间跨域扩展,战场态势复杂多变,尤其是当重大危机发生时,对军事决策的时效性提出了更高要求,指挥员必须能够及时、果断、有效、准确地定下作战决心。指挥员军事决策的有效性不仅受主观因素的影响,还受客观因素的影响。通过对影响因素的分析,提出提高指挥员军事决策能力的有效途径,为军队指挥人才培养提供合理建议。     

A bicriterion approach to common flow allowances due window assignment and scheduling with controllable processing times

We investigate a single‐machine scheduling problem for which both the job processing times and due windows are decision variables to be determined by the decision maker. The job processing times are controllable as a linear or convex function of the amount of a common continuously divisible resource allocated to the jobs, where the resource allocated to the jobs can be used in discrete or continuous quantities. We use the common flow allowances due window assignment method to assign due windows to the jobs. We consider two performance criteria: (i) the total weighted number of early and tardy jobs plus the weighted due window assignment cost, and (ii) the resource consumption cost. For each resource consumption function, the objective is to minimize the first criterion, while keeping the value of the second criterion no greater than a given limit. We analyze the computational complexity, devise pseudo‐polynomial dynamic programming solution algorithms, and provide fully polynomial‐time approximation schemes and an enhanced volume algorithm to find high‐quality solutions quickly for the considered problems. We conduct extensive numerical studies to assess the performance of the algorithms. The computational results show that the proposed algorithms are very efficient in finding optimal or near‐optimal solutions. © 2017 Wiley Periodicals, Inc. Naval Research Logistics, 64: 41–63, 2017     

测试性增长中资源优化配置模型及求解

良好的测试性设计对系统维修性具有重要意义,测试性增长试验通过一系列测试性设计缺陷发现和纠正措施,可保证系统测试性指标达到设计要求。针对基于延缓纠正的测试性增长过程中的资源配置问题进行研究,基于增长试验目标是否明确和试验资源是否受限制问题构建资源优化配置模型,并提出一种基于拉格朗日松弛和本地搜索的快速优化算法。仿真结果表明:该模型能够有效指导测试性增长中的资源优化配置问题,所提混合优化方法能够高效、准确地求解整数规划问题。     

基于累积前景理论的时间矩阵多属性综合评判模型

针对动态多属性方案排序优选决策问题,提出了一种基于累积前景理论的时间矩阵序列动态多属性综合评判系统模型。依据累积前景理论,将时间矩阵序列动态多属性决策转化为关于属性期望的益损时间矩阵序列动态决策,综合考虑各属性权重、各时间点的权重,计算各方案的最终综合前景值,并依据其大小对方案进行综合评判排序。最后给出了一个决策实例。     

一种基于分层智能混合决策的多Agent框架

智能对于指挥决策模型非常重要,也是提高模拟训练和作战实验质量的关键。作战中指挥决策内容多,问题性质不一,对智能要求高。在分析指挥控制模型发展现状基础上提出模型对智能的需求,分析设定了智能的5个层次,提出一种混合Agent框架,并在框架中对每一层智能给出解决方案,框架兼具认知Agent、BDI Agent和刺激反应Agent的优长,对构建智能化的训练和实验系统有较大帮助。框架在指挥装备作战运用实验中得到应用,具有较好的理论意义和应用价值。     

基于决策树的空中目标威胁分类研究

将决策树ID3算法应用到空中目标威胁程度分类问题。分析了现有威胁评估的常用方法及其局限性,论述了ID3算法在此问题上的适用性。以28批典型目标的威胁程度为例,对数据进行概化处理后,利用weka平台进行仿真,构造决策树,并从中挖掘出隐含的24条规则,这对决策支持系统知识库的构建是非常必要的,也为指挥员定下作战决心提供重要参考。     

子弹均匀散布子母弹最有利火力分配方法

在将子弹均匀散布的椭圆区域等效转换为矩形区域的基础上,建立了适宽射向射击子母弹对矩形目标毁伤全概率计算的数学模型。通过函数转换和泛函分析给出了子弹均匀散布子母弹理想射击密度,得到了理想射击密度下对目标的毁伤概率计算公式。讨论了最有利火力分配方式的确定方法,给出了最优射向间隔和表尺差的计算公式,为便于实际应用,给出了最优火力分配计算中所需的3个参数的近似计算公式,最后给出了应用算例。