A stochastic approximation algorithm to compute bid prices for joint capacity allocation and overbooking over an airline network

In this article, we develop a stochastic approximation algorithm to find good bid price policies for the joint capacity allocation and overbooking problem over an airline network. Our approach is based on visualizing the total expected profit as a function of the bid prices and searching for a good set of bid prices by using the stochastic gradients of the total expected profit function. We show that the total expected profit function that we use is differentiable with respect to the bid prices and derive a simple expression that can be used to compute its stochastic gradients. We show that the iterates of our stochastic approximation algorithm converge to a stationary point of the total expected profit function with probability 1. Our computational experiments indicate that the bid prices computed by our approach perform significantly better than those computed by standard benchmark strategies and the performance of our approach is relatively insensitive to the frequency with which we recompute the bid prices over the planning horizon. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011     

Signature based analysis of m‐Consecutive‐k‐out‐of‐n: F systems with exchangeable components

In this article, we study reliability properties of m‐consecutive‐k‐out‐of‐n: F systems with exchangeable components. We deduce exact formulae and recurrence relations for the signature of the system. Closed form expressions for the survival function and the lifetime distribution as a mixture of the distribution of order statistics are established as well. These representations facilitate the computation of several reliability characteristics of the system for a given exchangeable joint distribution or survival function. Finally, we provide signature‐based stochastic ordering results for the system's lifetime and investigate the IFR preservation property under the formulation of m‐consecutive‐k‐out‐of‐n: F systems. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011     

Annealing adaptive search,cross‐entropy,and stochastic approximation in global optimization

The Annealing Adaptive Search (AAS) algorithm for global optimization searches the solution space by sampling from a sequence of Boltzmann distributions. For a class of optimization problems, it has been shown that the complexity of AAS increases at most linearly in the problem dimension. However, despite its desirable property, sampling from a Boltzmann distribution at each iteration of the algorithm remains a practical challenge. Prior work to address this issue has focused on embedding Markov chain‐based sampling techniques within the AAS framework. In this article, based on ideas from the recent Cross‐Entropy method and Model Reference Adaptive Search, we propose an algorithm, called Model‐based Annealing Random Search (MARS), that complements prior work by sampling solutions from a sequence of surrogate distributions that iteratively approximate the target Boltzmann distributions. We establish a novel connection between MARS and the well‐known Stochastic Approximation method. By exploiting this connection, we prove the global convergence of MARS and characterize its asymptotic convergence rate behavior. Our empirical results indicate promising performance of the algorithm in comparison with some of the existing methods. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011     

边防武警执行命令行为的性质定位

边防武警具有军人和警察的双重属性,因此,其执行命令行为在性质上不同于普通的军事命令,而体现出一定的行政性特征。边防武警执行命令行为的性质定位直接关系到其执行多样化军事任务中各项行为的法律后果,具有重要的理论意义和司法实践意义。     

The knapsack problem with a minimum filling constraint

We study a knapsack problem with an additional minimum filling constraint, such that the total weight of selected items cannot be less than a given threshold. The problem has several applications in shipping, e‐commerce, and transportation service procurement. When the threshold equals the knapsack capacity, even finding a feasible solution to the problem is NP‐hard. Therefore, we consider the case when the ratio α of threshold to capacity is less than 1. For this case, we develop an approximation scheme that returns a feasible solution with a total profit not less than (1 ‐ ε) times the total profit of an optimal solution for any ε > 0, and with a running time polynomial in the number of items, 1/ε, and 1/(1‐α). © 2012 Wiley Periodicals, Inc. Naval Research Logistics, 2013     

基于二阶锥规划的MIMO雷达任意形状旁瓣波束设计

针对MIMO雷达非均匀布阵引起的高旁瓣问题,提出一种基于二阶锥规划的任意形状旁瓣设计算法.算法通过在旁瓣区域附加多个二次不等式约束,从而有效抑制干扰、保持期望方向天线增益的同时,获得了期望的、可以任意控制的旁瓣波束响应.通过将优化模型转化为凸二阶锥规划问题,利用内点法可以得到有效求解.仿真试验表明:算法能够获得任意形状期望旁瓣波束设计的同时,有效提高了波束形成器的输出信干燥比.最后计算机仿真结果证实了算法的有效性和可行性.     

一种冲击信号调理电路的设计

针对测量冲击信号时,高冲击振动传感器输出的正负电荷信号不能直接被AD进行模数转换并且冲击信号中常常会混有噪声信号的问题,设计了一种包含电荷-电压转换、隔直放大、十阶抗混叠低通滤波等的冲击信号调理电路.并对冲击信号调理电路中各个模块进行了理论分析和节点参数的计算.通过实验:实际测量到的滤波曲线、冲击信号调理电路输出的电压都与理论计算值十分接近.此冲击信号调理电路能够成功地将传感器输出的正负电荷信号调理成一定范围内的电压信号,能够有效地滤除冲击信号中的高频噪声.     

二阶阶跃模型下导航信号畸变对接收机载噪比估计影响分析

载噪比作为评估导航信号质量的重要指标,直接影响着接收机的捕获、跟踪和电文解调性能。推导了基于国际民航组织二阶阶跃信号畸变模型下导航接收机采用相关、非相关处理时载噪比估计与畸变模型参数之间的解析表达式,并分析了畸变参数对导航接收机载噪比估计误差的影响。理论分析及仿真结果表明:数字畸变参数变化范围下导航信号载噪比变化在10-2量级,增大模拟畸变的振荡频率或者减小衰减频率将会提高载噪比估计值,模拟畸变参数变化引起的载噪比损耗可达0.1d BHz,畸变参数对载噪比估计的影响整体上都较小,因此导航接收机的载噪比输出不能作为监测导航信号是否异常的完备检测量。     

小子样条件下战斗部爆炸仿真数据可信性分析

高效毁伤战斗部试验次数受到严格控制,如何在小子样条件下分析仿真数据的可信性非常有必要。通过均匀试验得到了战斗部在地下目标腔室内部爆炸某点冲击波最大超压峰值,利用normplot及Lilliefors函数分析了仿真数据的分布特征,而后在已有检验方法的基础上,结合顺序统计量和均匀分布的特点提出了一种新的相容性验证思路。研究表明:该方法不受样本容量的限制,利用MATLAB程序编程可以快速判断仿真数据的可信性。     

Efficient algorithms for flexible job shop scheduling with parallel machines

Job shop scheduling with a bank of machines in parallel is important from both theoretical and practical points of view. Herein we focus on the scheduling problem of minimizing the makespan in a flexible two-center job shop. The first center consists of one machine and the second has k parallel machines. An easy-to-perform approximate algorithm for minimizing the makespan with one-unit-time operations in the first center and k-unit-time operations in the second center is proposed. The algorithm has the absolute worst-case error bound of k − 1 , and thus for k = 1 it is optimal. Importantly, it runs in linear time and its error bound is independent of the number of jobs to be processed. Moreover, the algorithm can be modified to give an optimal schedule for k = 2 .