Minimal LST representations of MAP(n)s: Moment fittings and queueing approximations

A Markovian arrival process of order n, MAP(n), is typically described by two n × n transition rate matrices in terms of rate parameters. While it is straightforward and intuitive, the Markovian representation is redundant since the minimal number of parameters is n² for non‐redundant MAP(n). It is well known that the redundancy complicates exact moment fittings. In this article, we present a minimal and unique Laplace‐Stieltjes transform (LST) representations for MAP(n)s. Even though the LST coefficients vector itself is not a minimal representation, we show that the joint LST of stationary intervals can be represented with the minimum number of parameters. We also propose another minimal representation for MAP(3)s based on coefficients of the characteristic polynomial equations of the two transition rate matrices. An exact moment fitting procedure is presented for MAP(3)s based on two proposed minimal representations. We also discuss how MAP(3)/G/1 departure process can be approximated as a MAP(3). A simple tandem queueing network example is presented to show that the MAP(3) performs better than the MAP(2) in queueing approximations especially under moderate traffic intensities. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 549–561, 2016     

Reliability of a two-dimensional demand-based networked system with multistate components

Existing research on multistate system reliability has mainly focused on one-dimensional systems such as parallel systems, linear sliding window systems, and linearly consecutively connected systems. However, two-dimensional networked systems widely exist in real-world applications such as lighting systems, monitoring systems, and computer network systems. This research considers a two-dimensional networked system consisting of multistate components. The system fails if the cumulative performance of any row or any column cannot meet a predetermined demand. A novel reliability evaluation algorithm is proposed for the considered two-dimensional networked system by extending the universal generating function technique. Furthermore, the proposed model and reliability evaluation algorithm are extended to a two-dimensional networked system with phased missions. The proposed models and algorithms are illustrated by a matrix heating system in a thermoforming machine.     

Revenue maximization in two-station tandem queueing systems

We study optimal pricing for tandem queueing systems with finite buffers. The service provider dynamically quotes prices to incoming price sensitive customers to maximize the long-run average revenue. We present a Markov decision process model for the optimization problem. For systems with two stations, general-sized buffers, and two or more prices, we describe the structure of the optimal dynamic pricing policy and develop tailored policy iteration algorithms to find an optimal pricing policy. For systems with two stations but no intermediate buffer, we characterize conditions under which quoting either a high or a low price to all customers is optimal and provide an easy-to-implement algorithm to solve the problem. Numerical experiments are conducted to compare the developed algorithms with the regular policy iteration algorithm. The work also discusses possible extensions of the obtained results to both three-station systems and two-station systems with price and congestion sensitive customers using numerical analysis.     

Optimal production ramp-up in the smartphone manufacturing industry

Motivated by challenges in the smartphone manufacturing industry, we develop a dynamic production ramp-up model that can be applied to economically satisfy nonstationary demand for short-life-cycle products by high-tech companies. Due to shorter life cycles and more rapid evolution of smartphones, production ramp-up has been increasingly critical to the success of a new smartphone. In the production ramp-up, the key challenge is to match the increasing capacity to nonstationary demand. The high-tech smartphone manufacturers are urged to jointly consider the effect of increasing capacity and decreasing demand. We study the production planning problem using a high-dimensional Markov decision process (MDP) model to characterize the production ramp-up. To address the curse of dimensionality, we refine Monte Carlo tree search (MCTS) algorithm and theoretically analyze its convergence and computational complexity. In a real case study, we find that the MDP model achieves revenue improvement by stopping producing the existing product earlier than the benchmark policy. In synthetic instances, we validate that the proposed MCTS algorithm saves computation time without loss of solution quality compared with traditional value iteration algorithm. As part of the Lenovo production solution, our MDP model enables high-tech smartphone manufacturers to better plan the production ramp-up.     

9/11: WHAT DID WE KNOW AND WHEN DID WE KNOW IT?

In February 1998, Osama Bin Laden published a signed statement calling for a fatwa against the United States for its having ‘declared war against God’. As we now know, the fatwa resulted in the unprecedented attack of 9/11. The issue of whether or not 9/11 was in any way predictable culminated in the public debate between Richard Clarke, former CIA Director George Tenet and the White House. This paper examines whether there was any evidence of a structural change in the terrorism data at or after February 1998 but prior to June 2001, controlling for the possibility of other breaks in earlier periods. In doing so, we use the standard Bai–Perron procedure and our sequential importance sampling (SIS) Markov Chain Monte Carlo (MCMC) method for identifying an unknown number of breaks at unknown dates. We conclude that sophisticated statistical time‐series analysis would not have predicted 9/11.     

基于FMECA的信息系统软件失效安全风险评估与预测

随着大量软件产品应用于信息系统,不可避免地增大了软件失效对系统安全的影响概率。本文以某信息系统文电收发的处理过程为例,引入FMECA法,对其文电收发处理过程中由软件造成的风险进行分析,同时分析了引起风险常见的失效模式,并引入Markov模型对软件失效造成系统严重危害的风险进行预测,通过Markov模型与FMECA法结合能够迅速定位软件失效的原因,减少因软件失效造成的系统损失。     

基于抽象隐马尔可夫模型的CGF路径规划识别

路径规划识别是一种以位置信息为输入的在线识别。为了使CGF能在仿真中识别对手的路径和终点目标,在分析路径规划层次的基础上引入了抽象隐马尔可夫模型的识别框架。针对标准模型在对手更改终点目标和自上而下规划时无法识别的问题,提出了一种顶层策略可变的抽象隐马尔可夫模型。为模型的顶层策略增加初始分布和策略终止变量,更改了策略终止变量间的依赖关系,使下层策略能被强制终止。给出了改进后DBN结构,并通过推导条件概率更新和RB变量抽样流程实现了模型的近似推理。仿真实验表明,改进模型能准确识别给定环境下的各类典型航迹,不仅在终点目标不变时能较好地维持标准模型的识别准确率,在提供足够的观测数据后还能很好地解决变目标识别问题。     

非高斯噪声下信号盲检测算法*

针对实际甚低频和超低频接收机不仅受非高斯噪声的影响,同时受到接收机内部和外部环境中高斯噪声影响的问题,对噪声采用高斯尺度混合分布和高斯分布的混合模型建模,根据混合模型的性质,设计了一种基于马尔可夫链蒙特卡罗方法的信号盲检测算法。盲检测算法在贝叶斯层次模型下,采用Gibbs抽样和M-H抽样更新参数,同步检测信道衰落系数、噪声模型参数和信号。算法迭代效率快、精度高。通过与最优检测性能比较,盲检测算法性能优异,对甚低频和超低频信号接收具有重要的现实意义。     

基于GSPN的编队网络化防空指挥流程研究

为了提高编队指挥员在信息化海战中的作战指挥效率和应变能力,利用广义随机Petri网与马尔可夫链的等价关系,得到一种Petri网与马尔可夫链理论相结合的指挥流程时间性能分析的新方法,通过对模型进行分析与求解,得出了舰艇编队网络化防空作战指挥流程活动的准确作战周期,从而为指挥流程的优化提供有力的支撑,对评估系统效能水平也具有重要指导意义。     

一种针对基因识别的GHMM简化算法

广义隐Markov模型是计算机基因识别的一种重要模型,它克服了传统隐Markov模型的状态段长成几何分布的缺陷,更加适合于计算机基因识别。其缺点在于计算量大,需要采用有效的简化算法。利用基因的结构特点,在不附加额外限制条件的情况下,提出了一种新的简化算法,其计算复杂度是序列长度的线性函数。对实际生物序列数据的测试结果表明了此简化算法的有效性。