地空导弹反导作战拦截次数计算模型及仿真

在分析地空导弹武器系统拦截来袭目标过程的基础上,通过分析地空导弹发射区纵深建立了地空导弹武器系统可拦截次数模型.利用该模型可计算地空导弹武器系统对单个来袭目标在杀伤区内的拦截次数.仿真结果揭示了地空导弹武器系统在不同目标特性(速度、高度、航路捷径)下对目标的可拦截次数的影响.     

刑法罪名“口袋化”倾向之反思

我国刑法中的一些罪名在司法认定过程中,日益具有＂口袋化＂的倾向。这一倾向与罪刑法定原则、尊重保障人权的基本理念相冲突。这一倾向产生的原因主要有：部分刑法条文的规定不够明确;司法工作人员对于犯罪构成要件以及刑法基本理论的理解存在偏差;以社会危害性为中心的传统刑法观的影响;个别司法解释有违刑法的基本规定和基本原理等。应结合这一倾向产生的原因,通过各种途径对其加以遏制。     

舰空导弹射击模型优化分析

针对不同情况来袭的反舰导弹,运用系统分析的方法尝试在一定的条件下解决舰空导弹优化射击问题。通过分析,确立了在所假定的情况下选择各自不同的射击方法可以获得最大的抗击效果,从而提高作战的效费比。文中所确立的射击模型有一定的合理性和可行性,在射击方法恰当的情况下,舰空导弹武器系统的战斗可靠性对舰空导弹武器系统的射击效果起着重要作用。     

A produce‐to‐stock system with advance demand information and secondary customers

We consider a manufacturer (i.e., a capacitated supplier) that produces to stock and has two classes of customers. The primary customer places orders at regular intervals of time for a random quantity, while the secondary customers request a single item at random times. At a predetermined time the manufacturer receives advance demand information regarding the order size of the primary customer. If the manufacturer is not able to fill the primary customer's demand, there is a penalty. On the other hand, serving the secondary customers results in additional profit; however, the manufacturer can refuse to serve the secondary customers in order to reserve inventory for the primary customer. We characterize the manufacturer's optimal production and stock reservation policies that maximize the manufacturer's discounted profit and the average profit per unit time. We show that these policies are threshold‐type policies, and these thresholds are monotone with respect to the primary customer's order size. Using a numerical study we provide insights into how the value of information is affected by the relative demand size of the primary and secondary customers. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

On the effectiveness of top‐down strategy for forecasting autoregressive demands

We investigate the relative effectiveness of top‐down versus bottom‐up strategies for forecasting the demand of an item that belongs to a product family. The demand for each item in the family is assumed to follow a first‐order univariate autoregressive process. Under the top‐down strategy, the aggregate demand is forecasted by using the historical data of the family demand. The demand forecast for the items is then derived by proportional allocation of the aggregate forecast. Under the bottom‐up strategy, the demand forecast for each item is directly obtained by using the historical demand data of the particular item. In both strategies, the forecasting technique used is exponential smoothing. We analytically evaluate the condition under which one forecasting strategy is preferred over the other when the lag‐1 autocorrelation of the demand time series for all the items is identical. We show that when the lag‐1 autocorrelation is smaller than or equal to 1/3, the maximum difference in the performance of the two forecasting strategies is only 1%. However, if the lag‐1 autocorrelation of the demand for at least one of the items is greater than 1/3, then the bottom‐up strategy consistently outperforms the top‐down strategy, irrespective of the items' proportion in the family and the coefficient of correlation between the item demands. A simulation study reveals that the analytical findings hold even when the lag‐1 autocorrelation of the demand processes is not identical. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2007.