基于网格技术的仿真资源实时调度系统研究与实现

针对仿真资源服务运行中存在的性能瓶颈,以WSR工厂模式为基础,设计并实现了一种基于网格技术的仿真资源实时调度系统。该系统通过设置代理工厂服务,在客户端创建资源实例,实现了对仿真资源服务的实时动态调度和对资源属性的监控。原型实验证明,该系统具有调度时机合理、调度机制对服务方与客户端透明、资源监控机制不影响客户端操作和接口开放等优点。     

Scheduling parallel dedicated machines with the speeding‐up resource

We consider a problem of scheduling jobs on m parallel machines. The machines are dedicated, i.e., for each job the processing machine is known in advance. We mainly concentrate on the model in which at any time there is one unit of an additional resource. Any job may be assigned the resource and this reduces its processing time. A job that is given the resource uses it at each time of its processing. No two jobs are allowed to use the resource simultaneously. The objective is to minimize the makespan. We prove that the two‐machine problem is NP‐hard in the ordinary sense, describe a pseudopolynomial dynamic programming algorithm and convert it into an FPTAS. For the problem with an arbitrary number of machines we present an algorithm with a worst‐case ratio close to 3/2, and close to 3, if a job can be given several units of the resource. For the problem with a fixed number of machines we give a PTAS. Virtually all algorithms rely on a certain variant of the linear knapsack problem (maximization, minimization, multiple‐choice, bicriteria). © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008     

被动传感器融合检测系统的优化分布密度

被动传感器由于采用无源探测,而且制造成本低,体积小,在战场环境中将采用并配置大量的被动传感器来检测目标.不论是主动还是被动式传感器,在实际应用中均存在资源优化配置问题.针对被动传感器的具体应用模型,采取了若干合理近似,得到了在"OR"融合检测条件下,基于最大检测概率的被动传感器优化配置密度计算公式.     

Two-agent scheduling with linear resource-dependent processing times

This paper considers a two-agent scheduling problem with linear resource-dependent processing times, in which each agent has a set of jobs that compete with that of the other agent for the use of a common processing machine, and each agent aims to minimize the weighted number of its tardy jobs. To meet the due date requirements of the jobs of the two agents, additional amounts of a common resource, which may be in discrete or continuous quantities, can be allocated to the processing of the jobs to compress their processing durations. The actual processing time of a job is a linear function of the amount of the resource allocated to it. The objective is to determine the optimal job sequence and resource allocation strategy so as to minimize the weighted number of tardy jobs of one agent, while keeping the weighted number of tardy jobs of the other agent, and the total resource consumption cost within their respective predetermined limits. It is shown that the problem is -hard in the ordinary sense, and there does not exist a polynomial-time approximation algorithm with performance ratio unless ; however it admits a relaxed fully polynomial time approximation scheme. A proximal bundle algorithm based on Lagrangian relaxation is also presented to solve the problem approximately. To speed up convergence and produce sharp bounds, enhancement strategies including the design of a Tabu search algorithm and integration of a Lagrangian recovery heuristic into the algorithm are devised. Extensive numerical studies are conducted to assess the effectiveness and efficiency of the proposed algorithms.     

一体化联合登陆作战中船坞登陆舰编队展开点确定研究

在一体化联合登陆作战中,大型两栖战舰及其编队通常是敌首选的打击目标.为了确保船坞登陆舰编队的安全,在突击上陆过程中正确确定其展开点是其中一个重要的问题,它关系到登陆兵能否快速上陆和作战能否按计划顺利进行.该文从定量分析的角度对船坞登陆舰编队展开点确定问题进行了探讨,建立了其数学模型,并用一个实例予以说明.     

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。     

基于云桌面的系统部署模式创新

文章论述云桌面特点,对传统分散部署模式和集中部署模式进行比对,对各种客户端的优劣进行分析,结合战区信息系统对GPU的实际需求,综合运用显卡穿透和显卡分散模式,创新战区系统部署模式。     

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。     

基于数字地形分析的智能兵力部署

兵力部署是指挥员组织实施战斗的重要工作,也是实现战斗决心的重要条件。在分析兵力部署原则基础上,结合兵种配置的地形条件和战术条件,提出了兵力部署适应度函数。采用遗传算法,设计了适应于数字地形分析的编码方案。实验证明,遗传算法能够有效解决兵力部署的智能化自动生成和评价问题,方法可行。