Impact of investing in quality improvement on (Q,r, L) model involving the imperfect production process

This paper investigates the impact of quality improvement on the modified lot size reorder point models involving variable lead time and partial backorders. The formulated models include the imperfect production process and an investing option of improving the process quality. The objective is simultaneously optimizing the lot size, reorder point, process quality level and lead time. We first assume that the lead time demand follows a normal distribution, then relax this assumption to consider the distribution-free case where only the mean and standard deviation of lead time demand are known. An algorithm procedure of finding the optimal solution is developed, and two numerical examples are given to illustrate the results.     

The effect of different data structures,sample sizes on model fit measures

In this study, our aim was to investigate the changes of different data structures and different sample sizes on the structural equation modeling and the influence of these factors on the model fit measures. Examining the created structural equation modeling under different data structures and sample sizes, the evaluation of model fit measures were performed with a simulation study. As a result of the simulation study, optimization and negative variance estimation problems have been encountered depending on the sample size and changing correlations. It was observed that these problems disappeared either by increasing the sample size or the correlations between the variables in factor. For upcoming studies, the choice of RMSEA and IFI model fit measures can be suggested in all sample sizes and the correlation values for data sets are ensured the multivariate normal distribution assumption.     

社会发展道德成本论

本文从分析人类行为产生的利益基础和道德的经济特性入手,论证了社会发展中道德成本的存在和实施道德优化调控的必要与可能。并通过对社会政治、经济、生活的道德成本问题进行分析,指出道德成本优化要通过唤起道德自觉、完善保障条件、营造道德氛围等途径完成。     

Kiefer–Wolfowitz algorithm under quasi-associated random errors

In this article, we study the algorithm of Kiefer–Wolfowitz underquasi-associated random errors. We establish the complete convergence and obtain an exponential bound. Additionally, we build a confidence interval for the minimum. Numerical examples are sketched out to confirm the theoretical results and show the accuracy of the algorithm.     

New methods for fitting multiple sinusoids from irregularly sampled data

A novel framework is proposed for the estimation of multiple sinusoids from irregularly sampled time series. This spectral analysis problem is addressed as an under-determined inverse problem, where the spectrum is discretized on an arbitrarily thin frequency grid. As we focus on line spectra estimation, the solution must be sparse, i.e. the amplitude of the spectrum must be zero almost everywhere. Such prior information is taken into account within the Bayesian framework. Two models are used to account for the prior sparseness of the solution, namely a Laplace prior and a Bernoulli–Gaussian prior, associated to optimization and stochastic sampling algorithms, respectively. Such approaches are efficient alternatives to usual sequential prewhitening methods, especially in case of strong sampling aliases perturbating the Fourier spectrum. Both methods should be intensively tested on real data sets by physicists.     

Selective Maintenance in System Reliability with Random Costs of Repairing and Replacing the Components

In this paper we consider the problem of determining the optimum number of repairable and replaceable components to maximize a system's reliability when both, the cost of repairing the components and the cost of replacement of components by new ones, are random. We formulate it as a problem of non-linear stochastic programming. The solution is obtained through Chance Constrained programming. We also consider the problem of finding the optimal maintenance cost for a given reliability requirement of the system. The solution is then obtained by using Modified E-model. A numerical example is solved for both the formulations.     

全日制兽医专业硕士课程体系优化原则与途径——以山东农业大学为例

根据全日制兽医专业硕士学位的培养目标,以及目前全日制兽医硕士专业学位课程设置,指出了当前课程体系存在的实践课程比重偏小、选修课局限性太强和课程设置过于死板等问题,并结合兽医专业硕士的自身特点和培养原则从专业课程、选修课程、实践课程三方面提出了课程体系的理论性与专业性、广泛性与实用性、技术性与前沿性相结合等优化原则,以及如何实现兽医专业硕士课程体系的合理化、多元化和专业化的三大途径。     

Unconstrained parametrizations for variance-covariance matrices

The estimation of variance-covariance matrices through optimization of an objective function, such as a log-likelihood function, is usually a difficult numerical problem. Since the estimates should be positive semi-definite matrices, we must use constrained optimization, or employ a parametrization that enforces this condition. We describe here five different parametrizations for variance-covariance matrices that ensure positive definiteness, thus leaving the estimation problem unconstrained. We compare the parametrizations based on their computational efficiency and statistical interpretability. The results described here are particularly useful in maximum likelihood and restricted maximum likelihood estimation in linear and non-linear mixed-effects models, but are also applicable to other areas of statistics.     

间歇用汽负荷的蒸汽优化调度方法

当蒸汽系统中存在着间歇用汽设备时，其用汽负荷将随用汽设备的启停而产生波动．蒸汽负荷的急剧波动不仅影响锅炉的经济运行，而且直接影响蒸汽品质和产品质量．本文提出用数学规划的方法对间歇用汽设备的开启时间进行合理调度，从而使整个系统的用汽负荷趋于均衡．此方法在实际应用中取得了良好的效果．     

交互效应矩阵理论及应用

交互效应矩阵理论,是一种定性与定量相结合的控制理论,用以解决和处理一些运用经典数学的方法难以解决的模糊信息问题.本文将交互效应矩阵理论引入教学控制范畴,研究和分析学校的教学问题.实践证明这是一种行之有效、效果显著的教育控制理论,具有重要的推广价值.