用PERT法求解不确定网络中活动时间参数的改进研究

基于计划评审技术(PERT)中β分布的假设,阐述了常规三时估计方法的不足,提出了相应解决对策,即采用限定概率三时估计法分别建立了β分布和Gamma分布拟合方差最小模型,用微粒群算法求解模型,并且将求解结果与常规三时估计方法进行了对比研究。实例证实,对于不同类型的活动,可以选择合理的分布类型并利用限定概率三时估计法,对各个活动的持续时间服从的分布密度函数进行拟合,从而提高PERT的精度和效率。     

高职物流管理专业(港口与航运方向)课程体系研究——以新里程(天津)公司工作过程岗位技能为导向

以工作过程岗位技能为导向的课程体系设置是严谨的过程,需要在对实体企业的实际工作岗位技能进行调研后,才能展开具体课程设置。文章通过对新里程(天津)公司工作过程岗位技能进行数月调研后,根据该企业部门岗位职能拟定出高职物流管理专业(港口与航运方向)课程体系。     

工作记忆训练:研究现状及发展前景

工作记忆能够预测个体在认知加工任务中的表现,工作记忆容量具有相对稳定的特征,但近年来研究表明工作记忆容量可以通过训练得到改进。工作记忆训练直接影响额叶皮层、颅顶部皮层等大脑皮层活动以及中枢系统多巴胺受体浓度。工作记忆训练效应可以对未经过训练的工作记忆任务产生迁移,与工作记忆相关的神经网络亦能通过训练获得发展。工作记忆训练可以帮助个体减小因低工作记忆容量对学业成就、日常生活带来的影响。     

带时间窗的应急救助物资配送车辆路径优化模型研究

在自然灾害突发后,必须将救助物资在规定的时间内运送到受灾点。针对带有时间窗的应急救助物资配送车辆路径优化问题进行研究,建立车辆路径优化模型并运用了蚁群算法进行求解,取得了较好的效果。     

Distributed ARIMA models for ultra-long time series

Providing forecasts for ultra-long time series plays a vital role in various activities, such as investment decisions, industrial production arrangements, and farm management. This paper develops a novel distributed forecasting framework to tackle the challenges of forecasting ultra-long time series using the industry-standard MapReduce framework. The proposed model combination approach retains the local time dependency. It utilizes a straightforward splitting across samples to facilitate distributed forecasting by combining the local estimators of time series models delivered from worker nodes and minimizing a global loss function. Instead of unrealistically assuming the data generating process (DGP) of an ultra-long time series stays invariant, we only make assumptions on the DGP of subseries spanning shorter time periods. We investigate the performance of the proposed approach with AutoRegressive Integrated Moving Average (ARIMA) models using the real data application as well as numerical simulations. Our approach improves forecasting accuracy and computational efficiency in point forecasts and prediction intervals, especially for longer forecast horizons, compared to directly fitting the whole data with ARIMA models. Moreover, we explore some potential factors that may affect the forecasting performance of our approach.     

Filtration enlargement-based time series forecast in view of insider trading

This survey reviews filtration enlargement models in view of insider trading. Although filtration enlargement aptly models insiders' informational advantage, the theoretical results have not attracted the attention of the empiricists, owing mainly to the lack of a bridge transforming the results to testable hypotheses, and/or the absence of econometrics method linking the hypotheses and the data. This survey provides a feasible avenue to estimate insider information and to detect trading from a relatively sophisticated theoretical model, where the dynamics of publicly available data (e.g., stock price) implies insider information before the information is completely digested. We complete the survey with an empirical illustration based on simulated data.     

Towards a real-time prediction of waiting times in emergency departments: A comparative analysis of machine learning techniques

Emergency Departments (EDs) can better manage activities and resources and anticipate overcrowding through accurate estimations of waiting times. However, the complex nature of EDs imposes a challenge on waiting time prediction. In this paper, we test various machine learning techniques, using predictive analytics, applied to two large datasets from real EDs. We evaluate the predictive ability of Lasso, Random Forest, Support Vector Regression, Artificial Neural Network, and the Ensemble Method, using different error metrics and computational times. To improve the prediction accuracy, new queue-based variables, that capture the current state of the ED, are defined as additional predictors. The results show that the Ensemble Method is the most effective at predicting waiting times. In terms of both accuracy and computational efficiency, Random Forest is a reasonable trade-off. The results have significant practical implications for EDs and hospitals, suggesting that a real-time performance monitoring system that supports operational decision-making is possible.     

旅游形象认知的时间顺序对旅游目的地形象认知的影响研究--以银川沙湖与榆林红碱淖为例

旅游者对旅游目的地的形象认知是影响旅游活动的重要因素,对旅游目的地形象定位和市场营销策略的选择都具有重要的参考价值。许多因素对旅游目的地形象认知都产生影响,其中认知的时间特征对旅游目的地形象认知的影响是一个值得关注的新课题:旅游者对不同旅游目的地形象认知的先后顺序对旅游目的地形象认知是否产生影响以及这种影响的程度如何。文章以银川沙湖与榆林红碱淖为例,对旅游者对旅游目的地形象认知中的“先入为主”与“既成事实”效应进行了分析,初步证明旅游者对不同旅游目的地形象感知的先后顺序对旅游目的地形象认知产生影响,并针对案例景区的情况,强调了旅游形象定位与市场营销中的先发优势,提出了自己的观点。     

上市公司财务危机实时预警系统研究

我国加入WTO后，在给上市公司带来良好机遇的同时，也使其面临着严峻的挑战。笔者认为建立上市公司财务危机实时预警系统已势在必行。本文首先阐述了上市公司财务危机实时预警的基本前提，接着就如何架构上市公司财务危机实时预警的模式和机制作了探讨，并提出了相关的建议。     

基于JIT方式的中小企业在制品管理问题研究

在制品管理是中小企业生产过程中困扰已久的问题之一,尤其是在多工序、多品种制造业企业中。由于企业产品特征导致在制品管理的难度,JIT方式难以付诸实践。结合JIT思想与ERP系统,实现对在制品的物流与信息流有效控制,对提高生产效率、降低物流成本具有重要的意义。针对中小企业物料管理存在的问题及局限性进行分析,从而提出相关的改进建议。