Computing Aviation Sparing Policies: Solving a Large Nonlinear Integer Program

Deployed US Navy aircraft carriers must stock a large number of spare parts to support the various types of aircraft embarked on the ship. The sparing policy determines the spares that will be stocked on the ship to keep the embarked aircraft ready to fly. Given a fleet of ten or more aircraft carriers and a cost of approximately 50 million dollars per carrier plus the cost of spares maintained in warehouses in the United States, the sparing problem constitutes a significant portion of the Navy’s resources. The objective of this work is to find a minimum-cost sparing policy that meets the readiness requirements of the embarked aircraft. This is a very large, nonlinear, integer optimization problem. The cost function is piecewise linear and convex while the constraint mapping is highly nonlinear. The distinguishing characteristics of this problem from an optimization viewpoint are that a large number of decision variables are required to be integer and that the nonlinear constraint functions are essentially “black box” functions; that is, they are very difficult (and expensive) to evaluate and their derivatives are not available. Moreover, they are not convex. Integer programming problems with a large number of variables are difficult to solve in general and most successful approaches to solving nonlinear integer problems have involved linear approximation and relaxation techniques that, because of the complexity of the constraint functions, are inappropriate for attacking this problem. We instead employ a pattern search method to each iteration of an interior point-type algorithm to solve the relaxed version of the problem. From the solution found by the pattern search on each interior point iteration, we begin another pattern search on the integer lattice to find a good integer solution. The best integer solution found across all interations is returned as the optimal solution. The pattern searches are distributed across a local area network of non-dedicated, heterogeneous computers in an office environment, thus, drastically reducing the time required to find the solution.     

保险管理决策中的两个问题

本文运用风险决策理论建立了分保限额与红利分派两个保险管理决策问题的数学模型,从理论和实践两个方面讨论了最优管理策略,并给出了计算实例。     

随机需求条件下的延迟发运策略模型及性质

本针对随机需求条件下物流配送中心的库存和运输联合决策问题，在基本库存和自身运输能力不足的情况下，提出对剩余客户订货需求采取部分延迟到下一期与部分利用第三方物流立即发运两相结合的策略，并在具有一般惩罚(损失)费延迟发运量限制的条件下，建立运输和库存相关总成本数学期望最小的优化模型，论证了该模型的主要性质，在此基础上很容易构造求解该类问题的优化方法。     

721型分光光度计故障分析和维修

本文主要介绍了７２１型分光光度计所发生的一些故障现象和排除故障时分析，判断及检查结果。     

Bellman最优性原理——论动态规划(Ⅰ)

对于Bellman最优性原理,本文举出实例表明:(1)策略不一定有(合理的)子策略;(2)子策略不一定存在最优子策略;(3)最优策略不一定有最优子策略;(4)用最短路与反证法来论述最优性原理的正确性,不能肯定成立;(5)Bellman最优性原理与其递推公式并不等价。 讨论四类最优策略之后,给出最优性原理与递推公式等价的一个充分性定理。     

基于三维矩阵重构争论有向图的域间路由振荡检测算法

边界网关协议(Border G atew ay P rotoco l,BGP)是当前In ternet域间路由协议的事实标准,基于策略的路由选择过程使它不再是严格意义上的距离矢量协议,也不再具有距离矢量协议的收敛性.V aradhan指出,自治系统各自为政的策略配置方式会导致全局策略冲突,引起永久性的路由振荡.针对该问题,G riffin利用稳定路径问题(S tab le Path P rob lem,SPP)模型形式化的抽象出BGP协议行为,并以此为基础提出了一种分布式的策略冲突检测算法,尽管该算法完全避免了传统方法的缺陷,但仍然存在泄漏策略信息和对BGP协议改动太大以及浪费网络资源的问题.本文提出了一种基于安全多方计算的检测路由策略冲突的方法,用于在怀疑BGP路由发生振荡时,动态的检测系统中是否存在策略冲突.该方法采纳了G riffin的理论基础,同时由于巧妙的运用了安全多方计算协议,可以完全解决G riffin算法的问题,具有很好的实用性.     

检测实验室如何开展管理评审工作

详细论述了管理评审的基本概念、如何开展管理评审工作以及中国实验室国家认可委员会的有关政策。     

2003～2011年中国科普法规政策与化学普及书籍关系研究

2002年中国颁布科普法后,2003至2011年出版的化学普及书籍种数总体呈上升趋势。出版地和出版社总体上较为分散,局部集中度高。主题上基础知识类化学普及书籍最多,科学文化类次之,实用知识类较少,化学前沿类最少。涉及科普的一般法律、法规和政策对化学普及书籍的创作影响甚微。只有与国家约束性社会发展指标相关,发生涉及公民生命安全的重大社会事件,针对特定人群的具体实施政策出台,科普法规和政策才对化学普及书籍的出版起到推动作用。     

A model of longitudinal dispersion in non–homogeneous porous media

This paper is concerned with a model of the longitudinal dispersion of a set of tagged particles in a flow through nonhomogeneous stratified porous media. The model is a particular homogeneous Markov jump process, the successive states visited forming a non-homogeneous Poisson process. The passage time of alevel x of this Markov process is an additive stochastic process.This property is used to evaluate the general form of the longitudinal concentration function of the tagged particles in the flow.     

基于语义的软件服务故障自动诊断模型

模仿学习是机器人仿生机制研究的主要内容之一,即通过观察、理解、学习、模仿示教行为实现机器人的仿生特性。基于高斯过程分别表达采集离散示教信号所构成的示教轨迹和含有未知参数策略的模仿轨迹,构建模仿学习方法框架,将概率模型匹配引入到模仿学习中,以KL散度为代价函数比较两种轨迹的概率分布,运用梯度下降法寻求使KL散度最小的最优模仿控制策略,将策略应用于模仿机器人以完成与示教相同的模仿任务。以关节型机器人的机械臂摆动行为模仿为学习任务进行仿真,结果表明基于概率轨迹匹配的模仿学习方法能够实现机械臂摆动行为模仿,学习过程较传统方法简易且学习效果较好。