Software for dynamic reconfigurable order picking systems

This paper describes an approach, conceptual framework, and software architecture for dynamic reconfiguration of the order picking system. The research and development project was sponsored by the Material Handling Research Center (MHRC), a National Science Foundation sponsored Cooperative Industry/University Research Center. The storage configuration is assumed to be an in-the-aisle order picking system in which stockkeeping units (SKUs) can occupy variable capacity storage locations and stock-splitting is allowed among zones (clusters). The product mix may include multiple product families with different life cycles, correlated demand within families and commonality of demand across families.     

The generalized assortment and best cutting stock length problems

This paper introduces two new one-dimensional cutting stock models: the generalized assortment problem (GAP) and the best cutting stock length (BSL) problem. These new models provide the potential to reduce waste to values lower than the optimum of current models, under the right management circumstances. In the GAP, management has a standard length and can select one or more of any additional custom stock lengths, and management wishes to minimize cutting stock waste. This model is different from existing models that assume that the selection is from a small fixed set of stock lengths. In the BSL problem, management chooses any number of custom stock lengths, but wishes to find the fewest custom stock lengths in order to have zero waste. Results show waste reductions of 80% with just one custom stock length compared with solutions from standard cutting stock formulations, when item lengths are long relative to the stock length. The models are most effective when the item lengths are nearly as long as the stock length. Solutions from the model have been implemented for a manufacturer. The model is easily generalized to allow multiple existing stock lengths and different costs.     

存货审计应关注的问题及风险评价

存货的特点的及它在企业中的重要地位,决定了存货项目审计的重要性。审计人员在审计过程中应充分关注存货项目的异常现象,判断它的真实性、完整性;同时也要对存货进行分析,以评价企业存在的经营风险,从而控制审计风险。     

基于主成分的遗传神经网络股票指数预测研究

数据预测在金融投资领域占有重要地位,预测中输入变量的选取影响着预测的速度和精度,传统方法选取输入变量主观性较强,预测效果欠佳。将遗传算法与BP网络结合,利用GA的全局搜索优化BP网络的结构参数,有效克服BP算法的局部收敛等问题。使用主成分分析法选取输入变量,并将GA—BP混合建模应用于沪市综合指数预测中。实验结果表明,该方法改善了预测精度,达到了较好的预测效果。     

山东球墨铸铁管有限公司焦炭货场改造

山东球墨铸铁管有限公司350m^3高炉投产后,原有的焦炭货场场地小,而焦炭用量增大、供货厂家增多,无法做到混匀人炉。通过采取对焦炭进行分区管理和高架布料混匀改造后,焦炭质量趋向稳定;焦末的生成量从1318％降低到8．9％,提高了焦炭的利用率;同时焦比降低。     

基于B／S模式的汽车件零销存管理信息系统的设计与实现

对于采用连锁经营的汽车零件销售企业来说，各经营网点分布在同一城市的不同区域或不同城市，为了在充分利用原有信息和资源的基础上处理好许多中间环节，减少人员工作量。保证企业销存状态良好，提高管理效率，设计了基于B／S模式的汽车零件销存管理信息系统的技术方案，详细讨论系统的功能设计以及实现技术。     

改进的BP算法在股市预测中的应用

股票价格预测是证券界和学术界的一个重要的研究课题。神经网络具有强大的非线性逼近能力,文中采用MO-VLBP神经网络建立股票价格预测模型,对某银行的收盘价进行预测。实验结果证明,MO-VLBP网络模型应用于股票价格的短期预测,运算速度快、预测精度高。     

纸面色斑与纸张的最大信息容量

通过研究纸张的最大信息容量以及与其相关的信噪比,分析了作为承印材料的纸张对印刷品质量的影响．结果表明：影响印刷品质量的纸张信息容量是纸面所含影响油墨转移的呈色色斑平均尺度的敏感函数,从统计信息论的角度证实了萨雷尔马和奥蒂南的实验结果．     

Forecasting Method of Stock Market Volatility in Time Series Data Based on Mixed Model of ARIMA and XGBoost

Stock price forecasting is an important issue and interesting topic in financial markets.Because reasonable and accurate forecasts have the potential to generate high economic benefits,many researchers have been involved in the study of stock price forecasts.In this paper,the DWT-ARIMAGSXGB hybrid model is proposed.Firstly,the discrete wavelet transform is used to split the data set into approximation and error parts.Then the ARIMA(0,1,1),ARIMA(1,1,0),ARIMA(2,1,1)and ARIMA(3,1,0)models respectively process approximate partial data and the improved xgboost model(GSXGB)handles error partial data.Finally,the prediction results are combined using wavelet reconstruction.According to the experimental comparison of 10 stock data sets,it is found that the errors of DWT-ARIMA-GSXGB model are less than the four prediction models of ARIMA,XGBoost,GSXGB and DWT-ARIMA-XGBoost.The simulation results show that the DWT-ARIMA-GSXGB stock price prediction model has good approximation ability and generalization ability,and can fit the stock index opening price well.And the proposed model is considered to greatly improve the predictive performance of a single ARIMA model or a single XGBoost model in predicting stock prices.     

带残损原材料的二维优化下料问题的研究

本文将遗传算法和模拟退火算法相结合来求解二雏优化下料问题，下料件为不规则形状，原材料上带有残损，问题具有一般性。并在Windows2000平台下，用Matlab实现上述算法。通过实例验证了该算法的有效性。