M/M/m/m防空系统射击效能的排队概率特性

研究了具有消失制的M/M/m/m防空系统的射击效能,利用排队论及随机运筹学的有关知识,在模型的条件与假设下给出了其平稳状态的队长的分布律πk,平均工作的防空武器数E,敌机的突防概率πm,忙期长度等指标.     

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。     

对Geo/Geo/1抢占优先权排队平稳队长的分析

讨论了Geo/Geo/1抢占优先权排队模型,该模型可以用一个具有可数位相的拟生灭(QBD)过程来描述.对该过程,首先给出率算子以及联合平稳分布的结果.在此基础上,进一步得到了平稳状态时低优先权顾客数分布的概率母函数,并证明低优先权顾客数可以分解为两个相互独立的随机变量之和.     

具有N-策略和延迟单重休假且休假不中断的M/G/1排队系统

本文考虑具有N-策略和延迟单重休假且休假不中断的M/G/1排队系统.运用更新过程理论,全概率分解技术和Laplace变换工具,从任意初始状态出发,研究了队长的瞬态和稳态性质,获得了瞬态队长分布的Laplace变换的表达式和稳态队长分布的递推表达式.同时求出了稳态队长分布的概率母函数和附加队长分布的显示表达式.进一步讨论了当延迟时间Y=0,或Y→∞,或休假时间V=0时的特殊情形.最后,在建立费用结构模型下,由更新报酬过程理论获得了系统长期运行单位时间内所产生的成本期望费用的显示表达式,并通过数值实例讨论了使得系统在长期单位时间内的期望费用最小的最优控制策略N~*.     

分析M/M/1多重工作休假排队的一种新方法

用一种新方法对经典的M/M/1工作休假排队系统建立模型.对该模型,用无限位相GI/M/1型Markov过程和矩阵解析方法进行分析,不但得到了所讨论排队模型平稳队长分布的具体结果,还给出了平稳状态时服务台具体位于第几次工作休假的概率.这些关于服务台状态更为精确的描述是该排队系统的新结果.     

古典风险模型的破产概率与M/G/1忙期的最大工作量的分布

本文考虑了古典风险模型与排队论中M/G/1模型关系, 利用古典风险模型的破产概率导出了M/G/1中一个忙期内最大工作量的分布.     

具有两类失效模式的D-策略M/G/1可修排队系统分析

研究具有两类失效模式的D策略M/G/1可修排队系统,其中第一类失效是服务台在服务顾客期间发生的失效,第二类失效是服务台在空闲期间发生的失效,且两类失效模式的失效率不同.使用全概率分解技术和利用拉普拉斯变换与母函数等工具,从任意初始状态出发,讨论了系统队长的瞬时分布和稳态分布,获得了系统稳态队长分布的递推表达式与稳态队长的随机分解结果.进一步,在建立费用模型的基础上,通过数值计算实例讨论了使得系统在长期单位时间内达到最小值的最优控制策略D^*,并在同一组参数取值下与服务台不发生故障时的最优控制策略进行了比较.     

服务员多重假期中以概率p进入的M/G/1排队系统的进一步研究

考虑在多重假期中顾客到达以概率p(0-控制策略.首先通过数值实例分析了系统两个主要性能指标对系统参数p和T的敏感性,然后借用稳态队长分布{p_j,j-0,1,2…}讨论了系统容量的优化设计.其次,在建立费用结构模型的基础上,导出了系统在长期单位时间内的期望费用的显示表达式,并通过数值实例确定了在进入概率p固定时使系统单位时间期望费用最小的服务员休假时间的最优控制策略T*.最后,从系统的服务能力角度,在限制平均队长不超过某个固定正整数阀值L₀条件下,通过数值实例分别讨论了最佳进入概率p*和最佳休假时间T*.     

部分服务台同步多重休假的M/M/c/k排队及其优化

利用有限状态拟生灭过程和全概率分解的方法,首次研究了只允许部分服务台同步多重休假的M/M/e/k排队系统,得到了稳态队长和等待时间分布,并且讨论了系统的优化问题.     

具有Min(N,D)-策略控制的M/G/1可修排队系统及最优控制策略

考虑基于Min(N,D)-策略控制的M/G/1可修排队系统,其中服务台在服务员忙期中可能发生故障.使用全概率分解技术和拉普拉斯变换工具,讨论了系统的排队指标,同时重点讨论了服务台的一些可靠性指标,即服务台首次失效前的寿命分布、不可用度和(0,t]时间内的平均失效次数.最后,通过建立系统的费用模型,用数值计算实例讨论了最优控制策略(N~*,D~*).     

Parallel optimal control with multiple shooting, constraints aggregation and adjoint methods

In this paper, constraint aggregation is combined with the adjoint and multiple shooting strategies for optimal control of differential algebraic equations (DAE) systems. The approach retains the inherent parallelism of the conventional multiple shooting method, while also being much more efficient for large scale problems. Constraint aggregation is employed to reduce the number of nonlinear continuity constraints in each multiple shooting interval, and its derivatives are computed by the adjoint DAE solver DASPKADJOINT together with ADIFOR and TAMC, the automatic differentiation software for forward and reverse mode, respectively. Numerical experiments demonstrate the effectiveness of the approach.     

计及客户满意度的电动汽车物流配送路径规划与充放电管理

物流配送作为一种盈利型社会服务性行业,配送服务时间对客户满意度具有重要影响。论文考虑电动汽车(electric vehicle, EV)在配送途中和回到配送中心两个阶段,以物流配送成本最低和客户平均满意度最高为目标,构建了一种EV在换电模式下计及客户满意度的物流配送路径规划与充放电管理多目标优化模型,其中物流配送成本包括换电成本、车辆损耗成本以及慢速充放电成本。最后,以A-n29节点VRP基准测试系统插入四座换电站节点为例进行数值仿真,采用非支配排序遗传算法(Non-dominated sorting genetic algorithm, NSGA-II)对所提多目标优化模型进行求解,结果验证了所提方法的可行性和有效性。此外,论文进一步考查了EV慢速充放电管理对配电系统的影响,并对EV发车时间作了参数灵敏度分析,为管理者提供一些参考。     

Solution of the Falkner–Skan equation by recursive evaluation of Taylor coefficients

We present a computational method for the solution of the third-order boundary value problem characterized by the well-known Falkner–Skan equation on a semi-infinite domain. Numerical treatments of this problem reported in the literature thus far are based on shooting and finite differences. While maintaining the simplicity of the shooting approach, the method presented in this paper uses a technique known as automatic differentiation, which is neither numerical nor symbolic. Using automatic differentiation, a Taylor series solution is constructed for the initial value problems by calculating the Taylor coefficients recursively. The effectiveness of the method is illustrated by applying it successfully to various instances of the Falkner–Skan equation.     

效用信息熵在射击目标选择数学模型中的应用初探

针对装甲兵射击目标选择的特点,建立射击目标选择的数学模型,结合层次分析法,运用效用信息熵理论,通过实例不仅给出了射击目标的合理排序,还得到了影响射击目标选择的主、次要因素,可为装甲兵作战指挥自动化系统辅助决策提供一种新的方法.     

TRANSIENT SOLUTION FOR QUEUE-LENGTH DISTRIBUTION OF Geometry/G/1 QUEUEING MODEL

In this paper, the Geometry/G/1 queueing model with inter-arrival times generated by a geometric（parameter p） distribution according to a late arrival system with delayed access and service times independently distributed with distribution {gj }, j≥ 1 is studied. By a simple method （techniques of probability decomposition, renewal process theory） that is different from the techniques used by Hunter（1983）, the transient property of the queue with initial state i（i ≥ 0） is discussed. The recursion expression for u -transform of transient queue-length distribution at any time point n^＋ is obtained, and the recursion expression of the limiting queue length distribution is also obtained.     

An adaptive algorithm for solving stochastic multi-point boundary value problems

In this paper, we present an adaptive multiple-shooting method to solve stochastic multi-point boundary value problems. We first analyze the strong order of convergence of the underlying multiple shooting method. We then proceed to describe the proposed strategy to adaptively choose the location of shooting points. We analyze the effect of method parameters on the performance of the overall scheme using a benchmark linear two-point stochastic boundary value problem. We illustrate the effectiveness of this approach on several (one and two dimensional) test problems by comparing our results with other non-adaptive alternative techniques proposed in the literature.     

Initial states iterative learning for three-dimensional ballistic endpoint control

In this paper, an initial states iterative learning control algorithm is proposed for control of the ballistic endpoint displacement in three-dimensional space, where the target is moving and the projectile experiences system uncertainties. The characteristics of the three-dimensional ballistic process are formulated and explored, and the learning algorithm is proposed in the spatial domain. The algorithm consists of two parts. First, the initial speed and angles are iteratively learned to make the projectile attain a fixed position. Second, the shooting time is learned to tune the arrival time of the projectile. Since the dimensions of the solution space are larger than that of the task space, three control manners, including shooting speed, shooting angle and their combination, are researched respectively. Through rigorously analyzed, it is proved that the algorithm is convergent and the multiple initial states can be adjusted simultaneously. Finally, an example of practical cannonball projection is presented to verify the effectiveness of the proposed algorithms.     

Piecewise-Linear Pathways to the Optimal Solution Set in Linear Programming

An optimal control problem with four linear controls describing a sophisticated concern model is investigated. The numerical solution of this problem by combination of a direct collocation and an indirect multiple shooting method is presented and discussed. The approximation provided by the direct method is used to estimate the switching structure caused by the four controls occurring linearly. The optimal controls have bang-bang subarcs as well as constrained and singular subarcs. The derivation of necessary conditions from optimal control theory is aimed at the subsequent application of an indirect multiple shooting method but is also interesting from a mathematical point of view. Due to the linear occurrence of the controls, the minimum principle leads to a linear programming problem. Therefore, the Karush–Kuhn–Tucker conditions can be used for an optimality check of the solution obtained by the indirect method.