基于数据站场的系统效能评估决策支持方法

随着信息技术的发展,系统信息量的提升给军事系统效能评估带来了新的问题。针对系统效能分析数据非线性、高维数、强耦合的特点,提出了一种基于数据站场技术的效能评估决策支持方法。通过数据恢复、约减、求精与重构操作,对评估数据进行了处理,并搭建了数据站场立方体,降低了数据间的依赖性,满足了不同层面系统分析的需求。以面向任务的网络化火控系统的为例,进行了系统效能评估、时效性评估和指标分析,从三个层次验证了方法的有效性与可行性。将数据挖掘技术应用到仿真系统效能评估决策中,获得了更广泛的应用。     

考虑弹体动态特性的机动效率约束导引律

针对考虑交会角和过载约束导引律在大机动时能量损失大的问题,提出一种考虑导弹机动效率的多约束制导律。应用最优二次型原理推导出考虑一阶弹体延迟的时变导引系数闭环次优制导形式,将导弹机动时刻阻力系数引入时变权系数,并通过迭代确定机动效率约束边界。将时变约束表示成剩余时间与弹体延迟时间的函数,代入制导指令,进行弹道仿真。结果表明,对于常值与机动目标,文中制导律与过载约束导引律同只考虑交会角约束的导引律相比,对目标均能实现末端弹道成型要求,而考虑机动效率的制导指令分配更为合理,在避免指令加速度饱和的同时有效降低了拦截末端速度损耗,提高制导精度与毁伤效果。且该制导律中时变权系数无须配平求解,在保证精度的同时极大地提高了迭代速度。     

卫星在役考核相关问题研究

各类卫星在现代战争中发挥着重要和关键作用。我国传统的卫星试验和鉴定具有很强的科研试验性质,承制单位和用户重点关注的是性能检验,较少考虑作战效能和部队适用性等指标。为推动卫星装备建设和应用,装备试验鉴定部门初步组织开展了相应的在役考核工作。文章对比了美军装备作战试验与鉴定、我国作战试验与在役考核的概念;提出卫星在役考核的六大特点;分析卫星在役考核的组织方式、组织机构和组织保障的不足,提出相应发展建议;研究卫星在役考核指标体系设计、分析评估方法、问题反馈与整改等方面的不足,并提出相应措施建议。     

大力推进新时代中国特色科技兴军

习主席站在时代发展与战略全局的高度,着眼军队建设全局,深刻阐述了科技兴军在强军征程中的重要作用,明确了科技兴军的举措抓手,丰富和发展了新时代党的强军思想理论体系。必须毫不动摇地坚持走中国特色科技兴军之路,为实现党在新时代的强军目标提供强有力的支撑。     

基于窗口预测匹配的序列图像点目标轨迹检测算法

序列图像中运动点目标轨迹的实时检测算法,是目标识别、实时跟踪处理系统中的关键算法之一。在分析不同层次处理方法的基础上,给出了一种基于预测、窗口匹配的点轨迹预测—匹配检测算法,并根据仿真实验的效果,指出此算法可有效地对低信噪比序列图像运动点目标轨迹进行实时检测,具有多目标轨迹检测能力。     

Index policies for the routing of background jobs

Arriving (generic) jobs may be processed at one of several service stations, but only when no other (dedicated) jobs are waiting there. We consider the problem of how to route these incoming background jobs to make best use of the spare service capacity available at the stations. We develop an approximative approach to Whittle's proposal for restless bandits to obtain an index policy for routing. The indices concerned are increasing and nonlinear in the station workload. A numerical study testifies to the strong performance of the index policies developed. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004     

An adaptive forecasting algorithm and inventory policy for products with short life cycles

Products with short life cycles are becoming increasingly common in many industries, such as the personal computer (PC) and mobile phone industries. Traditional forecasting methods and inventory policies can be inappropriate for forecasting demand and managing inventory for a product with a short life cycle because they usually do not take into account the characteristics of the product life cycle. This can result in inaccurate forecasts, high inventory cost, and low service levels. Besides, many forecasting methods require a significant demand history, which is available only after the product has been sold for some time. In this paper, we present an adaptive forecasting algorithm with two characteristics. First, it uses structural knowledge on the product life cycle to model the demand. Second, it combines knowledge on the demand that is available prior to the launch of the product with actual demand data that become available after the introduction of the product to generate and update demand forecasts. Based on the forecasting algorithm, we develop an optimal inventory policy. Since the optimal inventory policy is computationally expensive, we propose three heuristics and show in a numerical study that one of the heuristics generates near‐optimal solutions. The evaluation of our approach is based on demand data from a leading PC manufacturer in the United States, where the forecasting algorithm has been implemented. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004.     

Flow shop scheduling with earliness,tardiness, and intermediate inventory holding costs

We consider the problem of scheduling customer orders in a flow shop with the objective of minimizing the sum of tardiness, earliness (finished goods inventory holding), and intermediate (work‐in‐process) inventory holding costs. We formulate this problem as an integer program, and based on approximate solutions to two different, but closely related, Dantzig‐Wolfe reformulations, we develop heuristics to minimize the total cost. We exploit the duality between Dantzig‐Wolfe reformulation and Lagrangian relaxation to enhance our heuristics. This combined approach enables us to develop two different lower bounds on the optimal integer solution, together with intuitive approaches for obtaining near‐optimal feasible integer solutions. To the best of our knowledge, this is the first paper that applies column generation to a scheduling problem with different types of strongly ????‐hard pricing problems which are solved heuristically. The computational study demonstrates that our algorithms have a significant speed advantage over alternate methods, yield good lower bounds, and generate near‐optimal feasible integer solutions for problem instances with many machines and a realistically large number of jobs. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004.     

Dynamic inventory and pricing models for competing retailers

We address infinite‐horizon models for oligopolies with competing retailers under demand uncertainty. We characterize the equilibrium behavior which arises under simple wholesale pricing schemes. More specifically, we consider a periodic review, infinite‐horizon model for a two‐echelon system with a single supplier servicing a network of competing retailers. In every period, each retailer faces a random demand volume, the distribution of which depends on his own retail price as well as those charged by possibly all competing retailers. We also derive various comparative statics results regarding the impact several exogenous system parameters (e.g., cost or distributional parameters) have on the equilibrium decisions of the retailers as well as their expected profits. We show that certain monotonicity properties, engrained in folklore as well as in known inventory models for centralized systems, may break down in decentralized chains under retailer competition. Our results can be used to optimize the aggregate profits in the supply chain (i.e., those of the supplier and all retailers) by implementing a specific wholesale pricing scheme. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2004.     

灾害应急反应建模与仿真

为了满足多智能体应急反应仿真的需求,首先对构建多智能体应急反应仿真模拟框架的复杂性进行了分析,在此基础上提出并设计了组件式仿真模拟框架.采用本体(Ontology)建模、语义网模型及产生式规则推理等理论方法构建了仿真模拟框架的场景建模模块、人员Agent建模模块等组件.最后对依照上述研究内容所开发的原型系统进行了测试,对测试结果进行了分析,结果表明该框架能够有效满足多智能体应急反应仿真的建模需求.