社区绩效影响因素的系统结构分析:基于信任与志愿精神视角

社区是社会管理的基本单元,其绩效的根源因素及其影响机制是人们关心的问题.基于信任和志愿精神视角,运用文献研究方法寻找社区绩效的影响因素,并结合ISM方法分析这些因素的系统结构,寻找影响社区绩效的根源因素及影响机制.研究表明,居民社团参与、社区文化、社会结构、居民人口特征、媒体使用及频率、基层政府管理模式以及居民对基层政府提供服务质量的期望与满意度是影响社区绩效的深层根源因素,其通过居民媒体信任、居民社会信任、居民基层政府信任、居民志愿精神、居民社区参与意识与行为以及居民社区自治意识与行为等中介变量来实现对社区绩效的影响.     

组织支持感及支持性人力资源管理对员工工作绩效的影响

通过对626名企事业单位大学以上文化程度知识员工的问卷调查,采用相关分析和结构方程模型的多重数据处理方法,构建和检验了多维组织支持感对于支持性人力资源管理影响员工工作绩效的中介作用模型。研究结果显示,支持性人力资源管理实践(具体包括上级支持、参与决策、组织公正等)对于提高员工组织支持感具有积极影响,同时,组织支持感在支持性人力资源管理实践与员工工作绩效(含任务绩效和情境绩效)之间起着重要中介作用。     

图书馆知识管理的多层次模糊综合评价方法

根据系统评价原则,采用调查问卷与数学定量研究相结合的处理方法,用模糊综合评价法来评价图书馆知识管理绩效.通过图书馆知识管理绩效评价的实证分析,证明评价指标的科学性、合理性以及切实可行性.     

制造企业服务化绩效评价指标体系研究

采用扎根理论方法建立指标全集,通过指标重要性分析、相关性分析以及信效度检验“三层筛选”,设计制造企业服务化绩效评价指标体系。研究结果表明,制造企业服务化绩效由产品绩效、顾客绩效、财务绩效和市场绩效4个维度构成,指标体系涵盖12个二级指标,具有较好的信度和效度。研究结论为企业评价和相关实证研究提供了理论依据。     

教学质量评估的统计分析法

教学评估是一项主观性很强的工作 ,因而是复杂的 ,其结果很可能偏离被评估对象的根本属性。为确保评估的公正、科学、准确 ,有必要对实测数据进行统计分析。本文首先采用稳定度检验和卡方检验来分析各项指标的评估结果是否具有可信性 ;其次 ,通过对各项指标评价结果的集中趋势进行统计分析 ,以检验量化误差是否在可接受的范围内 ;最后 ,运用符号秩次检验法对各项评估指标的权重是否合理进行统计分析。这些统计分析方法的应用 ,经实践证明是合理的 ,能够衡量评价结果的真实程度 ,从而保证了教学质量评估工作的顺利进行     

基于ZIP模型的零膨胀检验方法的比较研究

针对已存在关于零膨胀的R检验、C检验、Score检验、卡方检验、似然比检验、Wald检验和基于置信区间检验,通过Monte Carlo模拟分析方法,在功效和犯第一类错误比例的意义下,在不同零膨胀程度、不同均值和不同样本量下对上述检验作比较研究.得到了不同条件下7种检验方法的优良性,并结合理论对7种检验方法进行分析.     

因变量缺失下部分线性可加模型的估计和检验

本文研究部分线性可加模型在因变量存在缺失情形下的统计推断问题. 首先基于完整数据方法提出了参数分量的Profile 最小二乘估计并证明估计量的渐近正态性. 为了给出参数分量的区间估计,构造了渐近分布为卡方分布的经验似然统计量. 为了检验参数分量的线性约束条件, 构造了调整的广义似然比检验统计量, 当原假设成立时其渐近分布为卡方分布,从而将广义似然比检验推广到了缺失数据情形. 最后通过数值模拟验证所提方法的有效性.     

因变量缺失下部分线性可加模型的估计和检验（英文）

本文研究部分线性可加模型在因变量存在缺失情形下的统计推断问题.首先基于完整数据方法提出了参数分量的Profile最小二乘估计并证明估计量的渐近正态性.为了给出参数分量的区间估计,构造了渐近分布为卡方分布的经验似然统计量.为了检验参数分量的线性约束条件,构造了调整的广义似然比检验统计量,当原假设成立时其渐近分布为卡方分布,从而将广义似然比检验推广到了缺失数据情形.最后通过数值模拟验证所提方法的有效性.     

网络组织治理绩效影响因素的实证研究

本文从网络治理目标、治理结构、治理机制、治理环境四个方面分析网络治理绩效的影响因素,在具有典型网络合作特征的企业展开调查获得第一手资料,运用结构方程模型对网络治理绩效的影响因素进行实证检验。结果表明,成员企业之间的差异性越大、资源互补性越强、文化兼容性越好、关系资本强度越高,治理绩效越好;治理目标、治理环境和治理机制对治理绩效存在一定影响,但对我国企业而言具有其特殊性。     

Hausman检验统计量有效性的Monte Carlo模拟分析

在大样本下,Hausman检验统计量渐进地服从卡方分布,但在有限样本分析中却经常出现负值。对此,文献中并未形成一致看法。本文采用Monte Carlo模拟,从小样本偏误、内生性偏误、序列相关、非平行面板等角度,研究了Hausman检验统计量的小样本性质。结果表明,内生性问题(解释变量与个体效应相关)是导致Hausman统计量出现负值的主要原因。进一步分析表明,修正后的Hausman统计量,以及过度识别检验方法能够很好地克服上述缺陷,且具有很好的有限样本性质。     

Multimodal K-shortest viable path problem in Tehran public transportation network and its solution applying ant colony and simulated annealing algorithms

This paper treats with K-shortest viable path problem in a transportation network including multiple modes. The considered modes are metro, rapid-bus, private and walking. In this network, a viable path is one that the number of mode changes is limited and the traverse time and also the walking, metro and private usage are restricted subjected to some constraints. The traverse time is defined dependent on congestion level. Because constrained shortest path is NP-hard, we extend two metaheuristic algorithms namely GASA and BACS for the given problem. GASA is a Greedy Algorithm Simulated Annealing and BACS is a bi-direction searching Ant Colony System made by two colonies of ants. We evaluate the validation of these algorithms applying several multimodal random networks. In addition, their results are compared with CPLEX 12.1. We find that GASA is appropriate for small networks and BACS has better performance in median and large-scale networks. Our results on Tehran network also demonstrate that BACS provides better objective values than BACS ones because Tehran public transportation is sparse.     

A novel hybrid decomposition-ensemble model based on VMD and HGWO for container throughput forecasting

This paper built a hybrid decomposition-ensemble model named VMD-ARIMA-HGWO-SVR for the purpose of improving the stability and accuracy of container throughput prediction. The latest variational mode decomposition (VMD) algorithm is employed to decompose the original series into several modes (components), then ARIMA models are built to forecast the low-frequency components, and the high-frequency components are predicted by SVR models which are optimized with a recently proposed swarm intelligence algorithm called hybridizing grey wolf optimization (HGWO), following this, the prediction results of all modes are ensembled as the final forecasting result. The error analysis and model comparison results show that the VMD is more effective than other decomposition methods such as CEEMD and WD, moreover, adopting ARIMA models for prediction of low-frequency components can yield better results than predicting all components by SVR models. Based on the results of empirical study, the proposed model has good prediction performance on container throughput data, which can be used in practical work to provide reference for the operation and management of ports to improve the overall efficiency and reduce the operation costs.     

A locally linear neuro-fuzzy model for supplier selection in cosmetics industry

Supplier selection and evaluation is a complicated and disputed issue in supply chain network management, by virtue of the variety of intellectual property of the suppliers, the several variables involved in supply demand relationship, the complex interactions and the inadequate information of suppliers. The recent literature confirms that neural networks achieve better performance than conventional methods in this area. Hence, in this paper, an effective artificial intelligence (AI) approach is presented to improve the decision making for a supply chain which is successfully utilized for long-term prediction of the performance data in cosmetics industry. A computationally efficient model known as locally linear neuro-fuzzy (LLNF) is introduced to predict the performance rating of suppliers. The proposed model is trained by a locally linear model tree (LOLIMOT) learning algorithm. To demonstrate the performance of the proposed model, three intelligent techniques, multi-layer perceptron (MLP) neural network, radial basis function (RBF) neural network and least square-support vector machine (LS-SVM) are considered. Their results are compared by using an available dataset in cosmetics industry. The computational results show that the presented model performs better than three foregoing techniques.     

基于特征参数分布的机载电子设备故障诊断与预测

基于特征参数趋势进化的故障预测是一种有效的方法,引入了一种考虑特征参数概率分布的新型判据进行多故障模式诊断与预测.基于过程神经网络建立了高精度预测模型,根据模型和部件使用记录进行趋势预测.基于方法对机载电子设备进行案例研究,结果表明,方法的判定结果更加符合多故障模式并存、故障严重程度不同的实际情况,而具有较高拟和、泛化预测精度的PNN模型是一种有效的趋势预测方法.     

Commercial branch performance evaluation and results communication in a Canadian bank––a DEA application

In this paper, we focus on evaluating the performance of the commercial branches of a large Canadian bank using data envelopment analysis. Two production models are considered in this country-wide evaluation. One model, looking directly at resource usage, is most useful to the branch manager. The other model, incorporating financial results, is more geared towards senior management. We introduce non-discretionary factors to reflect specific aspects of the environment a branch is operating in, such as risk and economic growth rate of the region. Both input and output multipliers are constrained by incorporating market prices as well as managerial preferences, in order to get effectiveness measures. The cost-minimisation study led to valuable results pertaining to the performance of individual branches. Notable is the methodology introduced here that shows how to present graphical and numeric outcomes to managers. Gap maps, pie charts and target tables are produced for each branch to provide performance goals for the managers. Useful information has also been obtained at the district level. Output oriented models were analysed to reflect the Bank's recent emphasis towards growth in some areas.     

Some Results for Large Closed Queueing Networks with and without Bottleneck: Up- and Down-Crossings Approach

The paper provides the up- and down-crossing method to study the asymptotic behavior of queue-length and waiting time in closed Jackson-type queueing networks. These queueing networks consist of central node (hub) and k single-server satellite stations. The case of infinite server hub with exponentially distributed service times is considered in the first section to demonstrate the up- and down-crossing approach to such kind of problems and help to understand the readers the main idea of the method. The main results of the paper are related to the case of single-server hub with generally distributed service times depending on queue-length. Assuming that the first k–1 satellite nodes operate in light usage regime, we consider three cases concerning the kth satellite node. They are the light usage regime and limiting cases for the moderate usage regime and heavy usage regime. The results related to light usage regime show that, as the number of customers in network increases to infinity, the network is decomposed to independent single-server queueing systems. In the limiting cases of moderate usage regime, the diffusion approximations of queue-length and waiting time processes are obtained. In the case of heavy usage regime it is shown that the joint limiting non-stationary queue-lengths distribution at the first k–1 satellite nodes is represented in the product form and coincides with the product of stationary GI/M/1 queue-length distributions with parameters depending on time.     

基于风险网络的大型工程项目风险度量方法研究

风险度量是风险管理的基础,提出适合大型工程项目风险的风险度量方法.针对大型工程项目风险因素、风险信息、风险损失之间的复杂联系,构建大型工程项目风险网络,分别采用贝叶斯网络推理和网络层次分析法获得风险发生概率和风险量的估计,从而提出基于风险网络的大型工程项目风险度量方法.方法将风险损失量和风险损失发生概率进行了明确合理的结合,既可用于度量客观风险,也可用于度量主观风险.最后以槽菁头隧道施工风险管理为例说明该方法的具体应用步骤和效果.     

Understanding the value of upstream inventory information sharing in supply chain networks

Global competition has caused a paradigm shift in a firm’s outlook from a product-centric to a customer-centric view. With this shift, the availability of inventory at a store is a prime concern for firms in a supply chain network (SCN) as it affects customer goodwill and market share. Information sharing among the SCN partners is a key strategy to address this issue. In this study, we focus on the impact of sharing upstream inventory information in a SCN on its overall performance. A two-echelon SCN configuration with one retail store and two production facilities was used as an experimental test bed. To assess the marginal benefits of sharing additional information, three levels of information sharing were considered along with the base case of no information sharing. The information shared ranged from stock-out information at the lowest level to inventory and backorder levels at the highest level. Continuous Time Markov Chain models of the SCN were developed and analyzed to gain insights into the value of inventory information sharing. Numerical experiments were conducted to show that inventory information can be an effective substitute for physical inventory and to assess the impact of backorder limits on the SCN performance.     

动态环板元的动刚度矩阵

本文对于环形薄板单元取包含贝塞尔函数的谐振变形作为形状函数,解决了关于特殊函数的复杂积分问题.从而精确推导了环形单元的动刚度矩阵,并用直接刚度法进行了校核.接着,又着重于将封闭形式的动刚度矩阵,按频率平方的升幂式展开,得到了简洁完备的结果,以此作为结构动力特性分析和响应计算的基础.     

基于复杂网络的能源互联网信息物理融合系统跨空间风险传递研究

能源互联网呈现物理信息深度融合的趋势,为电力系统管理研究定义了新的研究框架。作为一个先进的复杂系统,能源互联网信息物理系统(Energy Internet Cyber-Physical System,ECPS)在其发展过程中面临着一些新的挑战,其中一个就是耦合结构下的风险管理。本文结合复杂网络理论和风险传递理论,着重在拓扑层面分析了ECPS跨空间交互机理,并在此基础上定义了交互路径和交互系数;接着建立了ECPS跨空间风险传递模型,量化描述了风险的传递和演化过程,并进行了风险影响评估;最后,通过仿真实验分析了三种不同交互系数节点故障的风险传递过程和不同攻击模式下系统的崩溃过程。对仿真结果的讨论阐述了能源互联网风险跨空间传递的特点,为更深入地研究ECPS的风险管理提供了参考。