装备备件管理技术综述

备件管理水平的高低直接影响着装备保障能力的发挥和使用保障费用的维持,备件管理现已成为世界范围内的研究热点。通过对国内外有关备件管理文献的分析,总结了备件管理在备件分类方法、需求预测与确定、库存控制与优化3个方面的研究现状。分析结果表明：备件管理将向多因素综合考虑的备件分类方法,基于备件需求不确定性的预测技术以及基于计算机辅助技术、物流和供应链管理等方向发展。     

巡航导弹攻击路径组的优化与评估

为保证对敌方目标实施多次有效攻击,巡航导弹攻击路径选择的关键在于寻找一组优化路径而不是一条优化路径(如果存在),既满足性能指标要求,又满足作战应用要求.在作战区域内任意两个匹配区之间飞行航迹已知(专题讨论)的情况下,就巡航导弹攻击路径组的优化与评估问题进行了探讨,分析了导弹作战运用要求和性能指标要求,建立了攻击路径组的优化与评估模型,计算结果表明:所给出优化与评估模型是可行的,能够满足巡航导弹实际作战要求.     

域间流量工程体系结构的研究

随着Internet的快速发展,为了提高网络整体资源的利用率,出现了通过跨域网络性能的优化,达到优化网络资源使用的域间流量工程技术。在分析与评价域间流量工程体系结构研究状况的基础上,提出了面向域间流量工程的RCP体系结构以及今后需要研究的主要问题。     

基于稳态遗传算法的间歇式覆盖天基雷达星座优化设计

针对天基雷达星座的构型优化设计,建立了能够反映星座重要工作性能的单双基地间歇式覆盖模型、星间链路模型、双基地雷达的动目标检测模型,得到了低轨道卫星星座星间链路判断准则,给出了统计评价特性和综合评估指标体系。在此基础上,建立星座设计的优化模型,采用基于可行解搜索法的协同演化遗传算法并融入稳态遗传进化策略,有效地处理带有复杂计算的目标函数和约束条件的星座优化问题,计算分析实例表明利用该方法进行星座设计是非常有效的。     

利用动态规划原理实现多冲量最优交会问题

多冲量最优交会问题是航天领域非常重要的一个研究方向,从运筹学角度将此问题转化为一个多阶段多维动态规划问题,并着重对多阶段二维动态规划进行分析研究。在给出状态方程及指标函数递推公式的基础上,得到了两冲量和三冲量交会问题最优解的求解算法,之后分别通过实例验证了算法的有效性。对于以地心角等参数为决策变量的更高维问题,讨论了利用进化算法等降维方法实现多维动态规划的思想。     

防空兵群指挥所配置方案优化分析

防空兵群指挥所配置是兵力部署的重要内容,它从根本上决定了指挥所的指挥效能和野战生存能力。多年来,指挥所的配置多是根据防空兵战斗的相关战术原则,由作战参谋提出数个配置方案,然后由指挥员做出决策。这种仅凭主观决策的做法与现代防空作战的要求不相适应。结合模糊多指标评判方法与优化理论,从发挥指挥效能与提高生存能力两个方面出发,对防空群指挥所的配置进行了分析,并建立了配置方案优化的模型,从根本上改变了指挥所配置完全依赖主观判断的做法。最后应用模型分析了一个实例。     

应用虚拟样机技术优化设计卡钳

分析了传统设计方法的弊端,介绍虚拟样机技术这一全新的设计方法.以应用动力学仿真软件ADAMS建造卡钳虚拟样机优化设计的实例,说明虚拟样机技术可以缩短开发周期、降低成本,提高设计质量和可靠性.     

Fragment spatial distribution of prismatic casing under internal explosive loading

Non-cylindrical casings filled with explosives have undergone rapid development in warhead design and explosion control. The fragment spatial distribution of prismatic casings is more complex than that of traditional cylindrical casings. In this study, numerical and experimental investigations into the fragment spatial distribution of a prismatic casing were conducted. A new numerical method, which adds the Lagrangian marker points to the Eulerian grid, was proposed to track the multi-material interfaces and material dynamic fractures. Physical quantity mappings between the Lagrangian marker points and Eulerian grid were achieved by their topological relationship. Thereafter, the fragment spatial distributions of the prismatic casing with different fragment sizes, fragment shapes, and casing geometries were obtained using the numerical method. Moreover, fragment spatial distribution experiments were conducted on the prismatic casing with different fragment sizes and shapes, and the experimental data were compared with the numerical results. The effects of the fragment and casing geometry on the fragment spatial distributions were determined by analyzing the numerical results and experimental data. Finally, a formula including the casing geometry parameters was fitted to predict the fragment spatial distribution of the prismatic casing under internal explosive loading.     

A guarantee rate optimization model for wastewater treatment system design under uncertainty

We consider a design problem for wastewater treatment systems that considers uncertainty in pollutant concentration levels at water sources. The goal is to optimize the selection of treatment technologies and pipeline connections, so that treated wastewater can achieve specified effluents discharge limits as well as possible. We propose a new two-stage model to optimize a set of guarantee levels, that is, the maximum concentration level of source pollutants for which treated wastewater can be compliant with discharge limits. In the first stage, treatment technologies and pipeline connections are selected. In the second stage, when pollutant concentration levels are revealed, wastewater distribution and mixing are determined. A key attractiveness of the proposed guarantee rate optimization model is that it can be simplified into a single-stage mixed-integer linear program. In our numerical experiments based on real-world pollutants data, the guarantee rate model demonstrates its advantages in terms of computational efficiency, scalability and solution quality, compared with the standard probability maximization model. Finally, the methodology proposed in this paper can also be applied to other two-stage problems under uncertainty with similar uncertainty characteristics.     

Spatial pricing and product allocation in online retailing

Spatial pricing means a retailer price discriminates its customers based on their geographic locations. In this article, we study how an online retailer should jointly allocate multiple products and facilitate spatial price discrimination to maximize profits. When deciding between a centralized product allocation ((i.e., different products are allocated to the same fulfillment center) and decentralized product allocation (ie, different products are allocated to different fulfillment centers), the retailer faces the tradeoff between shipment pooling (ie, shipping multiple products in one package), and demand localization (ie, stocking products to satisfy local demand) based on its understanding of customers' product valuations. In our basic model, we consider two widely used spatial pricing policies: free on board (FOB) pricing that charges each customer the exact amount of shipping cost, and uniform delivered (UD) pricing that provides free shipping. We propose a stylized model and find that centralized product allocation is preferred when demand localization effect is relatively low or shipment pooling benefit is relatively high under both spatial pricing policies. Moreover, centralized product allocation is more preferred under the FOB pricing which encourages the purchase of virtual bundles of multiple products. Furthermore, we respectively extend the UD and FOB pricing policies to flat rate shipping (ie, the firm charges a constant shipping fee for each purchase), and linear rate shipping (ie, the firm sets the shipping fee as a fixed proportion of firm's actual fulfillment costs). While similar observations from the basic model still hold, we find the firm can improve its profit by sharing the fulfillment cost with its customers via the flat rate or linear rate shipping fee structure.