军队会计流程存在的问题及优化策略

在新军事变革的背景下,军队会计流程存在不适应新管理体制需要、信息技术应用对会计流程再造力度不够、军队会计科目设置影响流程等问题。本文从界定军队会计流程的含义入手,提出了优化军队会计流程、建立智能化军队会计信息系统的四项策略,即重塑军队会计流程体系、重构军队会计信息系统、调整军队会计科目体系以及完善各流程会计处理方法。优化军队会计流程、建设智能化军队会计信息系统,不仅能够反映军事经济活动的历史信息,还能为不同类型的信息使用者提供预测、决策等支持,并有利于改善军队会计核算质量,准确反映各类资产负债的价值信息和单位的真实财务状况,对于提高军队会计信息质量具有一定的借鉴意义。     

Optimization of buckling load for laminated composite plates using adaptive Kriging-improved PSO: A novel hybrid intelligent method

An effective hybrid optimization method is proposed by integrating an adaptive Kriging (A-Kriging) into an improved partial swarm optimization algorithm (IPSO) to give a so-called A-Kriging-IPSO for maxi-mizing the buckling load of laminated composite plates (LCPs) under uniaxial and biaxial compressions. In this method, a novel iterative adaptive Kriging model, which is structured using two training sample sets as active and adaptive points, is utilized to directly predict the buckling load of the LCPs and to improve the efficiency of the optimization process. The active points are selected from the initial data set while the adaptive points are generated using the radial random-based convex samples. The cell-based smoothed discrete shear gap method (CS-DSG3) is employed to analyze the buckling behavior of the LCPs to provide the response of adaptive and input data sets. The buckling load of the LCPs is maximized by utilizing the IPSO algorithm. To demonstrate the efficiency and accuracy of the proposed methodology, the LCPs with different layers (2, 3, 4, and 10 layers), boundary conditions, aspect ratios and load patterns (biaxial and uniaxial loads) are investigated. The results obtained by proposed method are in good agreement with the literature results, but with less computational burden. By applying adaptive radial Kriging model, the accurate optimal results-based predictions of the buckling load are obtained for the studied LCPs.     

对流层散射无源监视系统最优布局方法

为提高对流层散射无源监视系统对辐射源的定位精度,利用改进粒子群优化算法对分布式监视节点进行最优布局设计。推导了基于电磁波方位到达角定位机制下的几何精度因子。在改进粒子群优化算法中,采用混沌理论初始化所有粒子的初始参数;通过自适应变化的惯性权重和学习因子来提高算法寻优能力;为防止粒子陷入局部最优,利用双子群机制进行寻优,并在两子群之间进行交叉变异操作,以增加粒子的多样性。仿真结果表明:相对于几种常规的布站方式,所提算法能够明显提高监视系统对辐射源的定位精度,运行时间较遍历寻优算法有所减少。     

Appointment scheduling at a multidisciplinary outpatient clinic using stochastic programming

The purpose of this paper is to investigate the problem of constructing an appointment template for scheduling patients at a specific type of multidisciplinary outpatient clinic called an integrated practice unit (IPU). The focus is on developing and solving a stochastic optimization model for a back pain IPU in the face of random arrivals, an uncertain patient mix, and variable service times. The deterministic version of the problem is modeled as a mixed integer program with the objective of minimizing a weighted combination of clinic closing time (duration) and total patient waiting time (length of stay). A two‐stage stochastic program is then derived to account for the randomness and the sequential nature of the decisions. Although it was not possible to solve the two‐stage problem for even a limited number of scenarios, the wait‐and‐see (WS) problem was sufficiently tractable to provide a lower bound on the stochastic solution. The introduction of valid inequalities, limiting indices, and the use of special ordered sets helped to speed up the computations. A greedy heuristic was also developed to obtain solutions much more quickly. Out of practical considerations, it was necessary to develop appointment templates with time slots at fixed intervals, which are not available from the WS solution. The first to be derived was the expected value (EV) template that is used to find the expected value of the EV solution (EEV). This solution provides an upper bound on the objective function value of the two‐stage stochastic program. The average gap between the EEV and WS solutions was 18%. Results from extensive computational testing are presented for the EV template and for our adaptation of three other templates found in the literature. Depending on the relative importance of the two objective function metrics, the results demonstrate the trade‐off that exists between them. For the templates investigated, the “closing time” ranged from an average of 235 to 275 minutes for a 300‐minute session, while the corresponding “total patient time in clinic” ranged from 80 to 71 minutes.     

动态威胁环境下单无人机测向定位航迹优化算法

为解决单架无人机在动态战场环境下的测向定位问题,提出了一种基于动态窗口法的单机测向定位航迹优化算法。以最大化Fisher信息矩阵行列式为测向定位评价准则,在由动态探测雷达和静/动障碍构成的动态战场环境中,基于动态窗口法思想,将测向定位航迹优化评价准则由传统的单步最优原则扩展到对多步预测航迹的评价,同时考虑雷达探测和静/动障碍环境对预测航迹的影响,通过滚动时域方法控制无人机最优航向。仿真结果表明,所提方法能够使无人机在有效逃避雷达探测威胁以及规避环境中静/动障碍的条件下保证对目标的高精度测向定位,为解决动态战场环境下的单架无人机测向定位问题提供了新思路。     

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。