Some distributions arising as a consequence of errors in inspection

In this article, the authors survey and consolidate their investigations during the years 1980-1983 dealing with consequences of errors in inspection sampling models. Some indication of the current and future research is given.     

Bayesian inference for a Lanchester type combat model

We undertake inference for a stochastic form of the Lanchester combat model. In particular, given battle data, we assess the type of battle that occurred and whether or not it makes any difference to the number of casualties if an army is attacking or defending. Our approach is Bayesian and we use modern computational techniques to fit the model. We illustrate our method using data from the Ardennes campaign. We compare our results with previous analyses of these data by Bracken and Fricker. Our conclusions are somewhat different to those of Bracken. Where he suggests that a linear law is appropriate, we show that the logarithmic or linear‐logarithmic laws fit better. We note however that the basic Lanchester modeling assumptions do not hold for the Ardennes data. Using Fricker's modified data, we show that although his “super‐logarithmic” law fits best, the linear, linear‐logarithmic, and logarithmic laws cannot be ruled out. We suggest that Bayesian methods can be used to make inference for battles in progress. We point out a number of advantages: Prior information from experts or previous battles can be incorporated; predictions of future casualties are easily made; more complex models can be analysed using stochastic simulation techniques. © 2000 John Wiley & Sons, Inc. Naval Research Logistics 47: 541–558, 2000     

Exploiting self‐canceling demand point aggregation error for some planar rectilinear median location problems

When solving location problems in practice it is quite common to aggregate demand points into centroids. Solving a location problem with aggregated demand data is computationally easier, but the aggregation process introduces error. We develop theory and algorithms for certain types of centroid aggregations for rectilinear 1‐median problems. The objective is to construct an aggregation that minimizes the maximum aggregation error. We focus on row‐column aggregations, and make use of aggregation results for 1‐median problems on the line to do aggregation for 1‐median problems in the plane. The aggregations developed for the 1‐median problem are then used to construct approximate n‐median problems. We test the theory computationally on n‐median problems (n ≥ 1) using both randomly generated, as well as real, data. Every error measure we consider can be well approximated by some power function in the number of aggregate demand points. Each such function exhibits decreasing returns to scale. © 2003 Wiley Periodicals, Inc. Naval Research Logistics 50: 614–637, 2003.     

登陆作战中直接火力准备的模糊指派模型

在登陆作战中,直接火力准备起着至关重要的作用,直接关系到登陆兵能否冲击上陆成功.考虑到实际作战中的模糊不确定性和多属性问题,利用模糊数学理论与多属性决策方法,将指派问题拓展到模糊条件下,针对登陆作战中如何发挥直接火力准备的最大效能问题,建立了模糊状态下的多目标指派模型,提供了求解方法,简要地对模型进行了动态化研究,并通过一仿真算例说明模型的应用效果较好,能为未来登陆作战提供辅助决策支持.     

一种评价伪装目标雷达发现概率的方法

为简化描述脉冲积累数、雷达参数及目标后向散射截面与发现概率、虚警概率之间的关系,结合伪装效果检测情况,提出了一种评价目标伪装前后雷达发现概率的方法,即构建评价函数对上述关系进行特征逼近研究.结果表明,该评价函数能反映伪装前后目标RCS与背景RCS比值的变化规律,且计算过程简单,可用于雷达检测评估.