The assignment of storage locations to containers for a container stack

Assigning storage locations to incoming or reshuffled containers is a fundamental problem essential to the operations efficiency of container terminals. The problem is notoriously hard for its combinatorial and dynamic nature. In this article, we minimize the number of reshuffles in assigning storage locations for incoming and reshuffled export containers. For the static problem to empty a given stack without any new container arrival, the optimum reshuffle sequence is identified by an integer program (IP). The integer program captures the evolution of stack configurations as a function of decisions and is of interest by itself. Heuristics based on the integer program are then derived. Their competitiveness in accuracy and time are established by extensive numerical runs comparing them with existing heuristics in literature and in practice as well as with extensions of the existing heuristics. Variants of the IP‐based heuristics are then applied to the dynamic problem with continual retrievals and arrivals of containers. Again, numerical runs confirm that the IP‐based heuristic is competitive. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2009     

贪心遗传算法解决一般武器-目标分配问题

一般武器-目标分配问题,是使武器发挥最大效能而使目标遭受最大毁伤的最优化问题.遗传算法广泛用于解决最优化问题.提出一种具有贪心优化机制的局部搜索方法,以提高遗传算法的搜索效率,从而迅速找到全局最优解.应用于炮兵武器-目标分配问题的仿真试验结果表明,此算法比现有的其他搜寻算法具有更好的求解效率.     

软件可靠性分配的遗传模型研究

对软件可靠性分配的遗传模型进行了研究,结合相关知识缩小了基本遗传算法的搜索区域,比较灵活地处理了非线性约束条件,提高了搜索效率,避免了早熟收敛现象.此外,在基本遗传算法中增加了保优操作,从而能够在理论上保证实现分配的最优化.算例表明,该算法十分有效.     

空天防御作战规划问题研究

从技术与作战结合的角度,系统分析空天防御作战系统和对抗特点等问题,构建了"态势组态—目标排序—目标分配—冲突动态最优预测"新型作战规划方法,提出"博弈控制理论与方法",论述了空天防御作战规划方法论。对空天防御作战规划理论研究有一定参考价值,为发展空天防御作战规划系统提供了研究思路和技术支撑。     

对失控翻滚目标任意位置悬停的模糊控制

针对失控翻滚目标任意位置悬停的控制问题进行研究。在目标器轨道坐标系下建立任意偏心率的三维相对运动方程,并分析失控翻滚目标的姿态运动特性。将悬停问题解耦为三个通道的二维模糊控制问题,并以x通道为例进行Mamdani型模糊控制器设计。确定相应的模糊子集、隶属度函数以及模糊控制规则表,采用面积中心法解模糊化。通过数值仿真验证了模糊控制器的控制性能。     

Doubly Stochastic Sequential Assignment Problem

This article introduces the Doubly Stochastic Sequential Assignment Problem (DSSAP), an extension of the Sequential Stochastic Assignment Problem (SSAP), where sequentially arriving tasks are assigned to workers with random success rates. A given number of tasks arrive sequentially, each with a random value coming from a known distribution. On a task arrival, it must be assigned to one of the available workers, each with a random success rate coming from a known distribution. Optimal assignment policies are proposed for DSSAP under various assumptions on the random success rates. The optimal assignment algorithm for the general case of DSSAP, where workers have distinct success rate distribution, has an exponential running time. An approximation algorithm that achieves a fraction of the maximum total expected reward in a polynomial time is proposed. The results are illustrated by several numerical experiments. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 124–137, 2016     

Approximation algorithms for sequential batch‐testing of series systems

We introduce and study a generalization of the classic sequential testing problem, asking to identify the correct state of a given series system that consists of independent stochastic components. In this setting, costly tests are required to examine the state of individual components, which are sequentially tested until the correct system state can be uniquely identified. The goal is to propose a policy that minimizes the expected testing cost, given a‐priori probabilistic information on the stochastic nature of each individual component. Unlike the classic setting, where variables are tested one after the other, we allow multiple tests to be conducted simultaneously, at the expense of incurring an additional set‐up cost. The main contribution of this article consists in showing that the batch testing problem can be approximated in polynomial time within factor , for any fixed . In addition, we explain how, in spite of its highly nonlinear objective function, the batch testing problem can be formulated as an approximate integer program of polynomial size, while blowing up its expected cost by a factor of at most . Finally, we conduct extensive computational experiments, to demonstrate the practical effectiveness of these algorithms as well as to evaluate their limitations. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 275–286, 2016     

对流层散射双向时间比对中对流层斜延迟估计

为精确估计对流层散射双向时间比对系统中对流层斜延迟,分析了估计卫星信号对流层斜延迟的Hopfield天顶延迟模型及CFA2.2映射函数模型并对其进行修正,从而适用于对流层散射斜延迟的精确估计。根据北纬35°~37°范围内的三个测站2010—2012三年的气象数据,验证Hopfield模型精度范围小于35 mm,并将三个测站按相互之间基线距离的不同分为三组比对站,利用2012年的气象数据,计算在不同入射角下一年的对流层散射斜延迟,并得出最大斜延迟对应的年积日和入射角。结果表明,三组比对站的最大单向散射斜延迟为21.82~35.45 m。在双向比对抵消90%的情况下,时间延迟为7.3 ns~11.7 ns;相互抵消95%时,时间延迟为3.6 ns~5.9 ns。     

面向多平台多目标协同跟踪的指派问题

针对多平台多目标协同跟踪中要求多个无人地面平台尽可能均匀地协同跟踪多个目标的特点,提出了改进的离散粒子群优化算法。首先采用连续型粒子群优化算法中的速度和位置迭代公式,然后对粒子位置进行离散编码,使粒子编码对应于可行的指派方案;其次,在优化算法中引入局部搜索,提高算法寻优性能。最后将所提算法应用于多平台多目标协同跟踪中的指派问题,并与未加入局部搜索的粒子群优化算法比较,仿真结果表明,加入局部搜索后的离散粒子群优化算法具有较好的寻优性能。     

连续波强激光武器的动态毁伤概率数学模型

在强激光武器的跟踪误差为均方可导、各态历经、零均值的正态过程的条件下,通过明晰的物理概念与严谨的数理演绎,导出了它的动态毁伤概率的级数表达式。通过算例探讨了动态毁伤概率的性质:它存在两个极大值所对应的随机穿越的自然频率,当跟踪系统工作在其中的非零频率上时,更具有快速反应能力;展现了跟踪误差的传递函数与动态毁伤概率间的定量关系。为论证、设计、检验强激光武器系统的动态毁伤概率提供了理论依据与技术支持。