For the rarely used spare parts, as the traditional predicting methods can't keep the high accurateness, the BP neural network is used to predict the rarely used spare parts demand. Firstly, the rarely used spare parts definition and its characteristics are given in this paper. Then the three layer BP neural network model is established, the back propagation algorithm is used as the learning algorithm. Finally, the rarely used spare parts-bus coupler consumption data is used for simulation analysis based on Guangzhou Subway line 3. The results show that the prediction is good.
The herd effect is a common phenomenon in social society. The detection of this phenomenon is of great significance in many tasks based on social network analysis such as recommendation. However, the research on social network and natural language processing seldom focuses on this issue. In this paper, we propose an unsupervised data mining method to detect herding in social networks. Taking shopping review as an example, our algorithm can identify other reviews which are affected by some previous reviews and detect a herd effect chain. From the overall perspective, the cross effects of all views form the herd effect graph. This algorithm can be widely used in various social network analysis methods through graph structure, which provides new useful features for many tasks.
Control model for spare parts inventory is established based on the optimal replenishment cycle. The replenishment cycle impact on the spare parts inventory control is analyzed as well as the demand forecasting. First, the optimal replenishment cycle is given by using the method of the lowest cost of inventory. Then according to the demand characteristic of the spare parts, the safety stock can be calculated. Finally on the basis of the demand forecasting, the calculation method of spare parts replenishment quantity is given. A numerical example is presented to verify the validity and practicability of the model.
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