Changing the paradigm: Simulation,now a method of first resort

Decades ago, simulation was famously characterized as a “method of last resort,” to which analysts should turn only “when all else fails.” In those intervening decades, the technologies supporting simulation—computing hardware, simulation‐modeling paradigms, simulation software, design‐and‐analysis methods—have all advanced dramatically. We offer an updated view that simulation is now a very appealing option for modeling and analysis. When applied properly, simulation can provide fully as much insight, with as much precision as desired, as can exact analytical methods that are based on more restrictive assumptions. The fundamental advantage of simulation is that it can tolerate far less restrictive modeling assumptions, leading to an underlying model that is more reflective of reality and thus more valid, leading to better decisions. Published 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 293–303, 2015     

多因素空间近距离巡查效能评估建模

空间近距离巡查可以开展对目标的近距离观测和监视,用于识别目标类型和工作状态等,对在轨服务、博弈对抗等军民领域均具有重要意义。分析了空间巡查任务的一般形式,并以近距离巡查最常用的光学观测为对象,构建了巡查观测的约束模型和相对距离因素评估模型、有效观测时间评估模型、目标观测角度评估模型等多因素的观测任务效能评估模型,解决了面向巡查任务全过程的综合效能评估问题,可以更好地支撑基于评估结果的巡查策略设计和巡查轨迹优化等。基于数值算例分析和半实物仿真实验,对提出的评估模型进行了仿真验证,结果显示实际观测效果和模型评估结果一致。     

一阶超对策模型中结局偏好认知信息集结的模式识别法

针对一类一阶n人超对策模型,提出了一种结局偏好认知信息的集结方法.首先通过对单一局中人中不同专家给出的对其他局中人结局偏好信息的不同认知进行关联分析,确定出专家的权重评价值,进而运用模糊模式识别原理,通过建立一个非线性规划模型,给出了一种新的超对策结局偏好认知信息的集结方法.最后,用一个数值例子对文中所提方法进行了说明.     

分布参数管道的一种改进的小分段数有限元模型

在分段数为１和２时，现有有限元模型的一阶谐振频率比一维分布参数模型的一阶谐振频率低许多，因而其适用的频率范围较低，计算精度较差。本文提出了一种改进的小分段数有限元模型，通过修正流体管道的并联导纳使分段数为１和２时的有限元模型的一阶谐振频率与一维分布参数模型一致，从而提高了其使用的频率范围和计算精度。利用单根管道阀门关闭的水击问题的仿真计算对改进模型的效果进行了验证。在分段数为２时，原有的有限元模型的计算结果与分布参数模型的计算结果相差较大，而改进后的模型的计算结果与分布参数模型的计算结果基本一致。     

固体推进剂药柱结构分析的非概率凸集合理论模型

为了研究不确定性能参数对固体推进剂药柱结构分析的影响 ,将非概率凸集合理论模型和粘弹性有限元相结合 ,以增量法处理遗传积分 ,利用摄动法预测其响应量区间 ,发展了一种适合药柱特点的不确定性方法。将其和随机结构分析进行对比 ,表明两种方法之间有一定的联系。     

指挥控制参考模型综述

综述指挥控制参考模型的起源、基本思想和目的,分析和阐释该模型的体系结构和运行过程。     

攻击型航空综合体作战效能分析的模型构成

给出了航空综合体及其作战效能的基本概念,并根据攻击型航空综合体作战特点及作战过程,阐述了对其作战效能进行分析时所需要建立的数学模型及各自应具有的主要功能     

Modeling and analysis of vessel casualties resulting from tanker traffic through narrow waterways

In this paper, we present a physics-based stochastic model to investigate vessel casualties resulting from tanker traffic through a narrow waterway. A state-space model is developed to represent the waterway and the location of vessels at a given time. We first determine the distribution of surface current at a given location of the waterway depending on channel geometry, bottom topography, boundary conditions, and the distribution of wind. Then we determine the distribution of the angular drift for a given vessel travelling at a given location of a waterway. Finally, we incorporate the drift probabilities and random arrival of vessels into a Markov chain model. By analyzing the time-dependent and the steady-state probabilities of the Markov chain, we obtain risk measures such as the probability of casualty at a given location and also the expected number of casualties for a given number of vessels arriving per unit time. Analysis of the Markovian model also yields an analytical result that shows that the expected number of casualties is proportional to square of the tanker arrival rate. We present our methodology on an experimental model of a hypothetical narrow waterway. © 1999 John Wiley & Sons, Inc. Naval Reseach Logistics 46: 871–892, 1999     

模型不可知的联合相互学习

主流的联邦学习(federated learning, FL)方法需要梯度的交互和数据同分布的理想假定,这就带来了额外的通信开销、隐私泄露和数据低效性的问题。因此,提出了一种新的FL框架,称为模型不可知的联合相互学习 (model agnostic federated mutual learning, MAFML)。MAFML仅利用少量低维的信息(例如,图像分类任务中神经网络输出的软标签)共享实现跨机构间的“互学互教”,且MAFML不需要共享一个全局模型,机构用户可以自定制私有模型。同时,MAFML使用简洁的梯度冲突避免方法使每个参与者在不降低自身域数据性能的前提下,能够很好地泛化到其他域的数据。在多个跨域数据集上的实验表明,MAFML可以为面临“竞争与合作”困境的联盟企业提供一种有前景的解决方法。     

Distribution free bounds for service constrained (Q,r) inventory systems

A classical and important problem in stochastic inventory theory is to determine the order quantity (Q) and the reorder level (r) to minimize inventory holding and backorder costs subject to a service constraint that the fill rate, i.e., the fraction of demand satisfied by inventory in stock, is at least equal to a desired value. This problem is often hard to solve because the fill rate constraint is not convex in (Q, r) unless additional assumptions are made about the distribution of demand during the lead‐time. As a consequence, there are no known algorithms, other than exhaustive search, that are available for solving this problem in its full generality. Our paper derives the first known bounds to the fill‐rate constrained (Q, r) inventory problem. We derive upper and lower bounds for the optimal values of the order quantity and the reorder level for this problem that are independent of the distribution of demand during the lead time and its variance. We show that the classical economic order quantity is a lower bound on the optimal ordering quantity. We present an efficient solution procedure that exploits these bounds and has a guaranteed bound on the error. When the Lagrangian of the fill rate constraint is convex or when the fill rate constraint does not exist, our bounds can be used to enhance the efficiency of existing algorithms. © 2000 John Wiley & Sons, Inc. Naval Research Logistics 47: 635–656, 2000