典型作战流程和分级环境下发射车生存能力改进ADC评估模型

为了综合评估发射车在作战中的生存能力,区分参数-性能-能力-效能四个层次建立武器装备评估空间,构建对象、内容和层次三个维度的评估框架,划分五类自然环境、三级威胁环境和三种作战状态,将外界环境引入评估过程,用被发现概率和损毁概率表征发射车隐蔽伪装能力和抗毁防护能力,建立发射车作战状态转移概率矩阵,提出典型作战流程和分级环境下的发射车生存能力改进可靠性、可信性、可用性综合评估模型。通过仿真分析,在不同环境和作战状态下各型发射车生存能力差异明显,模型能在性能空间、环境空间、作战流程空间多个维度动态评估发射车的生存能力,为发射车性能设计、战场运用     

An indicator of combat success

Assessing the effects of changes in weapons systems or battle tactics is difficult because of the variations in battles and the resulting instability of measures of combat effectiveness. Even under the relatively stable conditions of designed combat exercises, traditional measures may fail to reflect important battle events and dynamics. This variation in results makes the design, conduct, and evaluation of combat experiments, combat simulations, and combat training exercises a challenging endeavor, indeed. We develop and examine a combat measure of effectiveness, based on Lanchester models, which we call the battle trace. The battle trace is a measure of ongoing battle results, measured as a function of time into the battle. We describe how such measures might contribute to assessing effects of changing certain battle parameters. We suggest applications of these ideas to developing combat simulations and to enhancing combat training exercises.     

Location-allocation for distribution to a uniform demand with transshipments

This article analyzes the location-allocation problem for distribution from a single fixed origin via transshipment terminals to a continuous uniformly distributed demand. Distribution through terminals concentrates flows on the origin-to-terminal links and transportation economies of scale encourage the use of larger vehicles. Analytical expressions are derived for the optimal terminal locations, the optimal allocation of destinations to terminals, and the optimal transportation cost. Continuous analytic models assume either an allocation, by partitioning the service region into sectors, or terminal locations. This is unlikely to produce an optimal distribution system. The optimal cost is compared to the cost for suboptimal location-allocation combinations. Results indicate that the location decision is not too important if destinations are allocated optimally and that allocation to the nearest terminal may be poor, even with optimal locations. © 1992 John Wiley & Sons, Inc.     

通用防空反导火力优化分配方法

针对通用防空反导实时火力优化分配问题,分析了火力分配原则,综合考虑资源约束、空间关系约束以及人工干预等约束条件,以拦截效能最大、消耗费用最小为目标建立火力优化分配模型,并详细设计了拦截效能和消耗费用的表示模型;在该模型基础上,设计了一种基于离散粒子群的优化求解算法;通过仿真验证了模型的合理性和算法的可行性,对通用防空反...     

Simulation of stochastic activity networks using path control variates

This article details several procedures for using path control variates to improve the accuracy of simulation-based point and confidence-interval estimators of the mean completion time of a stochastic activity network (SAN). Because each path control variate is the duration of the corresponding directed path in the network from the source to the sink, the vector of selected path controls has both a known mean and a known covariance matrix. This information is incorporated into estimation procedures for both normal and nonnormal responses. To evaluate the performance of these procedures experimentally, we examine the bias, variance, and mean square error of the controlled point estimators as well as the average half-length and coverage probability of the corresponding confidence-interval estimators for a set of SANs in which the following characteristics are systematically varied: (a) the size of the network (number of nodes and arcs); (b) the topology of the network; (c) the percentage of activities with exponentially distributed durations; and (d) the relative dominance of the critical path. The experimental results show that although large improvements in accuracy can be achieved with some of these procedures, the confidence-interval estimators for normal responses may suffer serious loss of coverage probability in some applications.     

Cumulative search-evasion games

Cumulative search-evasion games (CSEGs) are two-person zero-sum search-evasion games where play proceeds throughout some specified period without interim feedback to either of the two players. Each player moves according to a preselected plan. If (X_t, Y_t,) are the positions of the two players at time t, then the game's payoff is the sum over t from 1 to T of A(X_t, Y_t, t). Additionally, all paths must be “connected.” That is, the finite set of positions available for a player in any time period depends on the position selected by that player in the previous time period. One player attempts to select a mixed strategy over the feasible T-time period paths to maximize the expected payoff. The other minimizes. Two solution procedures are given. One uses the Brown-Robinson method of fictitious play and the other linear programming. An example problem is solved using both procedures.     

Criteria and approximate methods for path-constrained moving-target search problems

A search is conducted for a target moving in discrete time among a finite number of cells according to a known Markov process. The searcher must choose one cell in which to search in each time period. The set of cells available for search depends upon the cell chosen in the last time period. The problem is to find a search path, i.e., a sequence of search cells, that either maximizes the probability of detection or minimizes the mean number of time periods required for detection. The search problem is modelled as a partially observable Markov decision process and several approximate solutions procedures are proposed. © 1995 John Wiley & Sons, Inc.