A multi‐stage stochastic programming approach for network capacity expansion with multiple sources of capacity

In networks, there are often more than one sources of capacity. The capacities can be permanently or temporarily owned by the decision maker. Depending on the nature of sources, we identify the permanent capacity, spot market capacity, and contract capacity. We use a scenario tree to model the uncertainty, and build a multi‐stage stochastic integer program that can incorporate multiple sources and multiple types of capacities in a general network. We propose two solution methodologies for the problem. Firstly, we design an asymptotically convergent approximation algorithm. Secondly, we design a cutting plane algorithm based on Benders decomposition to find tight bounds for the problem. The numerical experiments show superb performance of the proposed algorithms compared with commercial software. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 600–614, 2017     

An integer decomposition algorithm for solving a two‐stage facility location problem with second‐stage activation costs

We study a stochastic scenario‐based facility location problem arising in situations when facilities must first be located, then activated in a particular scenario before they can be used to satisfy scenario demands. Unlike typical facility location problems, fixed charges arise in the initial location of the facilities, and then in the activation of located facilities. The first‐stage variables in our problem are the traditional binary facility‐location variables, whereas the second‐stage variables involve a mix of binary facility‐activation variables and continuous flow variables. Benders decomposition is not applicable for these problems due to the presence of the second‐stage integer activation variables. Instead, we derive cutting planes tailored to the problem under investigation from recourse solution data. These cutting planes are derived by solving a series of specialized shortest path problems based on a modified residual graph from the recourse solution, and are tighter than the general cuts established by Laporte and Louveaux for two‐stage binary programming problems. We demonstrate the computational efficacy of our approach on a variety of randomly generated test problems. © 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010     

The building evacuation problem with shared information

In this article, the Building Evacuation Problem with Shared Information (BEPSI) is formulated as a mixed integer linear program, where the objective is to determine the set of routes along which to send evacuees (supply) from multiple locations throughout a building (sources) to the exits (sinks) such that the total time until all evacuees reach the exits is minimized. The formulation explicitly incorporates the constraints of shared information in providing online instructions to evacuees, ensuring that evacuees departing from an intermediate or source location at a mutual point in time receive common instructions. Arc travel time and capacity, as well as supply at the nodes, are permitted to vary with time and capacity is assumed to be recaptured over time. The BEPSI is shown to be NP‐hard. An exact technique based on Benders decomposition is proposed for its solution. Computational results from numerical experiments on a real‐world network representing a four‐story building are given. Results of experiments employing Benders cuts generated in solving a given problem instance as initial cuts in addressing an updated problem instance are also provided. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008     

大规模代谢网络分解的生物信息学研究

随着大规模分子相互作用数据的不断涌现,生物学网络方面的研究正日益得到重视.代谢网络处于生物体的功能执行阶段,其结构组成方式不仅反映了生物体的功能构成,也直接影响代谢工程中的途径分析和研究.作为代谢网络研究的重要环节,实现网络的合理分解不仅对于基因组范围内分子网络的结构和功能研究具有重要意义,也是代谢工程的途径分析和优化得以顺利进行的前提之一.在回顾代谢网络宏观结构和拓扑特征研究成果的基础上,通过对现有分解方法的深入分析,指出缺乏合理且有针对性的模型评估准则是目前网络分解研究中亟待解决的问题之一.今后的研究趋势在于如何整合更多的信息和发展更先进的分析方法,建立更合理的模型,并进一步拓展网络分解的应用范围.     

基于改进平方根UKF双向滤波的单站无源定位算法

针对单站无源定位可观测性弱、观测噪声大而导致的定位精度低、稳定性差和收敛速度慢等问题,在结合平方根无迹卡尔曼滤波(Square-Root Unscented Kalman Filter,SRUKF)以及后向平滑思想的基础上,提出了一种改进SRUKF的双向滤波算法。该算法采用Q-R分解的形式,使用误差协方差的平方根代替协方差参与递推运算,提高了算法的稳定性与运算效率。同时,该算法对状态向量进行扩维,将过程噪声与观测噪声通过非线性系统传播,降低了噪声对滤波精度的影响,并利用当前时刻滤波结果通过Rauch-Tung-Striebel(RTS)后向平滑得到再次前向滤波更高精度的起始值,提高了算法的定位精度与收敛速度。仿真结果表明,新算法在保证实时性的基础上改善了单站无源定位的性能。     

Military expenditure and income distribution in South Korea

This paper attempts to investigate the long-run and the causal relationship between military expenditure and income distribution in South Korea for the period 1965–2011. Applying the bounds test approach to cointegration, we found a long-run relationship between military expenditure and the Gini coefficient with military expenditure having a positive and a statistically significant impact on income inequality. A 1% rise in military expenditure increased the Gini coefficient by 0.38%. Application of the lag-augmented causality test also reveals a unidirectional causality running from military expenditure to income inequality. The evidence seems to suggest that devoting more resources to the military sector may further worsen income inequality in South Korea.     

结合振动特征优选和GWOA-XGBoost的电机轴承故障诊断

为解决电机轴承故障状态难以识别,从而造成诊断精度不高的情况,提出了一种基于信号特征提取与极限梯度提升算法(extreme gradient boosting, XGBoost)结合的电机轴承故障诊断模型。使用优化的变分模态分解获得振动信号的固有模态函数(intrinsic mode function, IMF)分量,再基于多尺度熵理论计算各IMF分量的多尺度熵值进行特征重构。在鲸鱼优化算法(whale optimization algorithm, WOA)中引入遗传算法的选择、交叉、变异操作对WOA进行改进。用改进的WOA算法对XGBoost的超参数进行寻优,获得了帮助XGBoost取得最优分类效果的超参数组合,将7种不同故障类型的振动信号进行重构后输入优化的XGBoost模型进行故障诊断。实验结果表明,所提GWOA-XGBoost模型的电机轴承故障诊断精度能够达到97.14%,相较于传统诊断方法,性能提升效果显著。     

顾及噪声影响的GNSS高程序列预测Prophet方法

全球导航卫星系统(global navigation satellite system, GNSS)高程时间序列具有非平稳、非线性、含噪声等特点,在深入研究Prophet预测模型的基础上,针对Prophet预测模型对于趋势信号和周期信号有良好预测效果这一特性,提出一种引入经验模态分解(empirical mode decomposition, EMD)的“降噪—分解—预测”组合GNSS高程时间序列预测方法。该方法先将原始时间序列进行EMD降噪,再对降噪后的序列进行分解预测,最后重构各分量预测信号为最终预测序列。通过对实测高程数据进行研究,实验结果表明：降噪后信号的平均信噪比为10.30dB,能量百分比平均为88.75%;利用所构建的短期预测方法,GNSS高程时间序列预测结果的均方根误差分别平均提升26.41%和14.88%;平均百分比误差分别平均提升18.92%和7.91%,验证了组合预测方法的有效性及实用性。     

多输出性能下的重要性测度指标及其求解方法

针对基于马氏距离的重要性测度存在的问题,提出了基于谱分解加权摩尔彭罗斯马氏距离的重要性测度指标,通过构造多输出协方差阵的广义逆矩阵以及谱分解的策略,有效解决了协方差阵求逆奇异情况以及由于未能充分考虑多输出之间的相互关系而导致的错误识别重要变量的问题,克服了基于马氏距离指标的局限性。数值算例与工程算例结果表明:所提重要性测度可以更加准确地获得输入变量对结构系统多输出性能随机取值特征贡献的排序,从而为可靠性设计提供充分的信息。     

Approximation of multiclass queueing networks with highly variable arrivals under deterministic routing

In this paper, we present the heavy‐traffic bottleneck phenomenon under multiclass deterministic routing and discuss how it can be addressed by decomposition approximation. Examples show that Bitran and Tirupati's method and Whitt's enhancements for deterministic routing may not properly account for this phenomenon. We propose refinements to these methods based on Whitt's variability functions. Results of numerical experiments on simple networks and semiconductor manufacturing process show significant improvement in the approximation of expected waiting time at bottleneck stations. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.