姿控发动机布局方式优化分析

在液体推进剂动力系统质量模型的基础上,针对采用双组元推进剂和挤压式输送系统的小推力空间飞行器姿控发动机,在控制总冲量和总冲量矩相同的情况下,对动力系统总质量最轻的姿控发动机最优布局方式进行了优化分析     

像管电子光学系统多目标优化的研究

将多重网格法用于静电像管电子光学场分布计算,采用改进的约束变尺度法进行静电聚焦像管的优化设计,较大地提高了电场分布的计算精度和像管设计效率,减少了计算时间和设计周期。研究的加权最小二乘法构造优化计算的多目标函数,普遍适用于调焦、定焦、变倍、选通和校畸变等各种类型静电像管的设计与计算。     

李雅普诺夫判据在锁相环设计中的应用

本文介绍一种严格判定非线性模型锁相环电路的稳定性方法──李雅普诺夫判据,给出了二阶、三阶非线性环的详细判定过程及设计原则,并对一些实际电路进行了讨论。     

高炮防空反导系统有关环节采样频率的分析与优选

从系统的精度、平稳性和硬件造价、计算机负担等方面对高炮防空反导系统中的目标测定器输出采样频率和棱镜伺服系统采样频率的取值进行了理论分析和计算机仿真,在此基础上给出了最佳取值范围。     

地面雷达故障诊断策略研究

本文对现役地面雷达的故障诊断方式进行了综合比较分析,提出了诊断策略优化的基本标准和要求,并推荐一种新的诊断方式——两级结合诊断方式。     

混合遗传算法及其在运载火箭最优上升轨道设计中的应用

运载火箭最优上升轨道设计问题是一类终端时刻未定、终端约束苛刻的最优控制问题,经典算法求解这类问题时收敛性差、局部收敛等问题表现得比较突出。针对上述问题,将具有良好全局收敛性的遗传算法应用到运载火箭最优上升段设计问题求解中,为了提高遗传算法的收敛速度和克服早熟问题,结合遗传算法和单纯型算法的优点,设计了两种混合遗传算法。计算结果表明,所设计的混合遗传算法是求解复杂问题的有效全局优化方法,可以成功地解决一类终端时刻可变飞行器最优控制问题。     

The gradual covering problem

In this paper we investigate the gradual covering problem. Within a certain distance from the facility the demand point is fully covered, and beyond another specified distance the demand point is not covered. Between these two given distances the coverage is linear in the distance from the facility. This formulation can be converted to the Weber problem by imposing a special structure on its cost function. The cost is zero (negligible) up to a certain minimum distance, and it is a constant beyond a certain maximum distance. Between these two extreme distances the cost is linear in the distance. The problem is analyzed and a branch and bound procedure is proposed for its solution. Computational results are presented. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004     

舰船装备修理规划方案的DEA分析与优化

运用DEA方法对舰船装备修理规划方案的有效性进行了分析,对非有效单元的改进途径进行了探讨,为舰船修理规划的制定提供了科学依据.     

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.