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本文研究毁伤目标离散时间的概率分布和PH表示.在射击时间间隔服从离散PH分布的条件下,本文导出了不考虑发现目标因素和考虑该因素二种情况下,毁伤目标时间的离散PH表示与分布.同时,文章用实例说明了求毁伤目标离散时间的PH表示、概率分布和毁伤目标平均离散时间的方法.     

搜索型随机格斗命中次数的分布与毁伤概率

本文研究了搜索型随机格斗模型，采用在时间[0，t]内射击命中目标次数的概率分布与毁伤概率指标来评定射击效果。文章对射击时间间隔随机变量服从的分布采用两种假设t指数分布与一般分布．对指数分布的假设，文章导出的结果比较完善；对一般分布的假设，文章导出了计算射击命中次数概率分布的计算公式。     

多因素影响下毁伤目标时间的分布与特征

文章研究搜索型随机格斗中，在射弹飞行时间、携弹数量有限因素影响下如何导出毁伤目标时间的分布与特征的问题，推导出在这些因素共同影响下，毁伤时间密度函数与特征函数的表达式。     

命中次数随机时毁伤时间分布与格斗获胜概率的研究

文章研究了一对一随机格斗中一类最具有一般性的模型——格斗双方带有搜索系统并且毁伤对方所需命中次数随机的格斗模型 .文章从研究条件随机过程入手 ,导出了格斗方毁伤对方所需时间的分布与相应的特征函数表达形式 ,也求出了计算获胜概率的公式     

需多发命中时毁伤时间的分布与特征

本研究带有搜索系统并且毁伤目标需要多发命中的格斗模型,并利用更新方程导出了毁伤目标所需时间的分布密度与特征函数。最后,章用实例说明了计算过程。     

系统首达控制域时间的离散分布特性

文章研究被控系统首达控制域时间的概率分布问题.通过对被控量的离散化处理并借助于近代发展起来的Phase-Type分布理论,求出了首达控制域时间的各阶条件矩,并将其转化为求解代数方程组.然后,求出了首达时间的条件L-S变换和条件分布.最后,说明了系统状态转移矩阵及PH分布的确定问题.整篇文章解决了首达控制域时间分布的描述与求解问题.     

具有位相型随机补货提前期的PH退化可修系统及其最优更换策略

将几何过程与PH分布相结合, 讨论一个带有位相型随机补货提前期的PH退化可修系统.通过建立最小生成元$Q$矩阵,获得了系统在稳态情形下的状态概率分布向量及其数值解.根据上述研究结果同时也得到了系统的几个重要可靠性指标. 进一步地,还考虑了基于部件故障次数的订购策略和更换策略,导出系统单位时间平均运行成本的解析表达式并给出一个确定最优$N$策略的数值算例.     

多级适应性休假排队系统的PH封闭性

对多级适应性休假的M/G/1排队系统,若休假时间服从位相型(PH)分布,我们证明了随机分解中的附加队长和附加延迟分别是离散和连续的PH随机变量,并给出其不可约PH表示,作为特例,国内外广泛研究的多重休假和单重休假系统,随机分解中的附加随机变量对PH分布都是封闭的。     

一类多对多随机格斗的获胜概率

为了计算出多对多随机格斗的获胜概率,首先推导出多对多随机格斗的状态转移速率,然后应用拉普拉斯变换的性质,分别计算出在不带搜索和带搜索两种情形下,三对二随机格斗中双方各自获胜概率的实用公式.     

同单调相依结构下两重生命模型的概率分布

在寿险实务中,在处理涉及到多个生命的问题时往往假设各个生命之间是独立的,但事实上,因为受某些相同因素影响的生命之间总是存在一定的正相依性．本文证明了在给定边际分布的二维随机向量中,同单调相依结构是在相关序意义下最强的正相依结构,研究了在此相依结构下的两重生命模型的概率分布,并给出了随机序意义下两个状态消亡时间的随机上界和随机下界．     

MATADOR: An Analytic Model of a Stochastic Tank Due

By constructing an analytic model of a stochastic tank duel, this paper provides a means for evaluating a number of key performance characteristics of duelling tanks. The model explicitly represents target detection and the time taken to acquire a target and fire, or reload and fire. Additionally, tacticalmanoeuvre and kill probabilities are included. The probability of each possible outcome of such a duel is derived as output from the model.     

一类搜索型随机格斗的获胜概率研究

把随机过程分析引入Lanchester方程就形成了随机格斗理论.运用随机格斗理论研究了潜艇协同隐蔽攻击水面舰艇编队获胜概率的数学模型,利用状态转移图和Laplace变换的性质推导出了2对2搜索型随机格斗中双方的获胜概率公式,并结合潜艇协同隐蔽攻击水面舰艇的实际,计算分析了格斗双方的获胜概率.利用这一公式可以得到概率上的精确解,能够被用来定量评估潜艇协同隐蔽攻击水面舰艇编队的作战效能.     

Probability Distribution of Solution Time in GRASP: An Experimental Investigation

A GRASP (greedy randomized adaptive search procedure) is a multi-start metaheuristic for combinatorial optimization. We study the probability distributions of solution time to a sub-optimal target value in five GRASPs that have appeared in the literature and for which source code is available. The distributions are estimated by running 12,000 independent runs of the heuristic. Standard methodology for graphical analysis is used to compare the empirical and theoretical distributions and estimate the parameters of the distributions. We conclude that the solution time to a sub-optimal target value fits a two-parameter exponential distribution. Hence, it is possible to approximately achieve linear speed-up by implementing GRASP in parallel.     

服务系统中位相型分布的随机比较

本文运用应用概率中的随机占优研究位相型(PH)分布的随机比较问题,具体给出在一阶、二阶随机占优下比较两个离散PH分布或两个连续PH分布的充分条件及充分必要条件。研究表明,比较两个离散PH分布可变性的条件与比较两个连续PH分布可变性的条件不同,在二阶随机占优意义下比较两个连续PH分布的条件与均值无关,而比较两个离散PH分布的条件与均值有关。本文的结果可用于研究PH分布的最小变异系数问题和可变性问题,也可用于研究带有PH到达间隔或PH服务的排队系统中到达过程或服务时间可变性对系统队长或等待时间的影响。     

Shield versus sword resource distribution in K-round duels

The paper considers optimal resource distribution between offense and defense in a duel. In each round of the duel two actors exchange attacks distributing the offense resources equally across K rounds. The offense resources are expendable (e.g. missiles), whereas the defense resources are not expendable (e.g. bunkers). The outcomes of each round are determined by a contest success functions which depend on the offensive and defensive resources. The game ends when at least one target is destroyed or after K rounds. We show that when each actor maximizes its own survivability, then both actors allocate all their resources defensively. Conversely, when each actor minimizes the survivability of the other actor, then both actors allocate all their resources offensively. We then consider two cases of battle for a single target in which one of the actors minimizes the survivability of its counterpart whereas the counterpart maximizes its own survivability. It is shown that in these two cases the minmax survivabilities of the two actors are the same, and the sum of their resource fractions allocated to offense is equal to 1. However, their resource distributions are different. In the symmetric situation when the actors are equally resourceful and the two contest intensities are equal, then the actor that fights for the destruction of its counterpart allocates more resources to offense. We demonstrate a methodology of game analysis by illustrating how the resources, contest intensities and number of rounds in the duels impact the survivabilities and resource distributions.     

Optimal Policies for Investment with Time-Varying Return Distributions

We develop a model in which investors must learn the distribution of asset returns over time. The process of learning is made more difficult by the fact that the distributions are not constant through time. We consider risk-neutral investors who have quadratic utility and are selecting between two risky assets. We determine the time at which it is optimal to update the distribution estimate and hence, alter portfolio weights. Our results deliver an optimal policy for asset allocation, that is, the sequence of time intervals at which it is optimal to switch between assets, based on stochastic optimal control theory. In addition, we determine the time intervals in which asset switching leads to a loss with high probability. We provide estimates of the effectiveness of the optimal policy.     

Exploiting run time distributions to compare sequential and parallel stochastic local search algorithms

Run time distributions or time-to-target plots are very useful tools to characterize the running times of stochastic algorithms for combinatorial optimization. We further explore run time distributions and describe a new tool to compare two algorithms based on stochastic local search. For the case where the running times of both algorithms fit exponential distributions, we derive a closed form index that gives the probability that one of them finds a solution at least as good as a given target value in a smaller computation time than the other. This result is extended to the case of general run time distributions and a numerical iterative procedure is described for the computation of the above probability value. Numerical examples illustrate the application of this tool in the comparison of different sequential and parallel algorithms for a number of distinct problems.