基于改进广义成本函数的可靠性分配优化

可靠性分配优化问题的难点在于影响分配的各类因素与可靠性指标间关系的描述与表达.从可靠性成本与各类影响因素的内在关系人手,通过改进广义成本函数进行各类影响因素的融合处理,构建得出新的可靠性分配优化模型实现可靠性指标的综合求解.     

THEORY AND APPLICATION TO THE US MILITARY

An ever‐growing share of defence R&D expenditures is being dedicated to the development and fielding of integrative technologies that enable separate individual systems to work in a coordinated and synergistic fashion as a single system. This study explores the optimal defence budget allocation to the development and acquisition of weapon systems and to the development of integrative technologies. We develop a suitable optimization framework, and then use it to derive the optimal budget allocation and analyse its properties. Finally, we use US defence budget data to calibrate the parameters of the model and provide a quantitative measure for the apparent US military supremacy.     

多目标多决策者环境下防空反导装备体系资源分配与优化

作战体系具有层次高、规模大,各作战分系统间协调配合密切的特征。在作战任务中,这种多层次、多系统、多决策者、多目标的结构使得对各作战子系统的武器装备分配变得更为复杂。为解决此类复杂结构的优化问题,在层次化多目标分析方法基础之上,将层次系统的风险管理引入到模型中来,形成多目标多决策者资源分配模型多目标多决策者资源分配模型,用以解决在不确定风险环境下进行多目标资源分配的问题。利用MOMDRA模型建模空袭风险下防空反导体系的武器装备分配问题,在求解该模型的过程中采用系统分解的思路,利用权重法,站在不同决策者的角度得到该体系的Pareto最优资源分配方案,并通过一个实例来说明此方法的可行性。     

Screening multi-dimensional heterogeneous populations for infectious diseases under scarce testing resources,with application to COVID-19

Testing provides essential information for managing infectious disease outbreaks, such as the COVID-19 pandemic. When testing resources are scarce, an important managerial decision is who to test. This decision is compounded by the fact that potential testing subjects are heterogeneous in multiple dimensions that are important to consider, including their likelihood of being disease-positive, and how much potential harm would be averted through testing and the subsequent interventions. To increase testing coverage, pooled testing can be utilized, but this comes at a cost of increased false-negatives when the test is imperfect. Then, the decision problem is to partition the heterogeneous testing population into three mutually exclusive sets: those to be individually tested, those to be pool tested, and those not to be tested. Additionally, the subjects to be pool tested must be further partitioned into testing pools, potentially containing different numbers of subjects. The objectives include the minimization of harm (through detection and mitigation) or maximization of testing coverage. We develop data-driven optimization models and algorithms to design pooled testing strategies, and show, via a COVID-19 contact tracing case study, that the proposed testing strategies can substantially outperform the current practice used for COVID-19 contact tracing (individually testing those contacts with symptoms). Our results demonstrate the substantial benefits of optimizing the testing design, while considering the multiple dimensions of population heterogeneity and the limited testing capacity.     

云计算安全协议设计

分析了目前云计算服务中存在的应用风险和安全隐患,并将其抽象为两方面的问题,即安全边界问题和用户服务资源映射问题。为了解决这两个方面的问题,根据云计算系统的实际需求,给出云计算服务的一种普适性的定义;在此定义基础上,提出了云计算服务的安全框架,并设计了云计算服务各层协议集合。在这个框架和协议集合下,分析了保证云计算服务正常执行所需的四方面要素,以及将诸要素较好地整合在一起的方法。     

带运力限制及单边时变约束下多出发机场航空战略投送兵力分配模型研究

利用民航力量由多个机场向一个目标机场实施航空战略投送时,由于各民航机场都具有运力限制约束,不可能无限制地快速投送兵力,因此,考虑了在多个出发机场存在运力限制约束的情况下,构建了"投送时间最短,动员机场最少"多目标决策模型,并给出了优化算法和算例.     

基于随机漫游模型的协同搜索力最优分配方法

针对多平台协同搜索的最优搜索问题,以多无人机区域搜索任务为背景,将搜索论和随机规划引入到编队协同搜索中,建立了环境模型、无人机模型、传感器模型;为了解决目标丢失后的“应召”搜索问题,建立了目标的随机漫游模型;通过对搜索力最优分配问题的分析,认为其实质为一类线性规划问题,提出了一种基于递归原理的解算方法;最后,通过仿真计算,证明了该随机漫游模型的合理性,并验证了所提算法的有效性。     

应用离散粒子群-郭涛算法分配多无人机协同任务

针对以往考虑时间窗约束的多无人机协同任务分配问题模型不能反映在有效时间窗内,任务执行时间对任务收益的影响及求解算法效率较低的问题。建立了将任务收益和任务执行时间直接联系起来的任务分配模型和可行解到粒子整数编码方式的映射,设计了混合离散粒子群-郭涛算法的组合优化问题求解策略。借助粒子群算法利用粒子自身信息和种群有用信息指导种群进化的本质特点,优化郭涛算法的适应性序列倒置操作;设计了可变的学习选择概率来选择个体的学习粒子,改进了序列倒置算子。仿真实验验证了该方法处理复杂任务分配问题的有效性。     

院校体制编制调整中的资源优化配置

面对武警新一轮院校体制编制调整,如何优化资源配置,有效规避各种风险,是当前院校需要重点解决的课题。通过调查研究,总结分析了人、财、物等院校资源在体制编制调整中面临的风险,有针对性地提出了人力、财力和物力资源优化配置及规避风险的途径、方法和措施。对于提高资源使用效益,促进院校体制编制调整顺利推进,具有积极的指导和借鉴意义。     

基于速度可控粒子群算法的航天测控系统任务可靠性分配

航天测控系统是一个典型的多阶段任务系统,讨论了可靠性分配时的约束条件和分配目标,构建了其任务可靠性分配模型,针对任务可靠性分配这类复杂的约束组合优化问题,提出了一种速度可控的粒子群优化算法.为克服粒子群算法的早熟问题,该算法引入了速度更新的方向控制规则和尺度控制规则用于增加群体的多样性,并根据两种控制规则,提出了种群粒子的速度更新策略.通过算例仿真,表明算法在用于航天测控系统任务可靠性分配问题时具有分配结果优、收敛速度快等优点.