Research on the Food Retailer Stock Forecasting based on the Genetic Optimization RBF Model

Xiaoheng Tao

Abstract


Because food is easy to corrosion, deteriorate and not easy to store, the food supply chain is more stringent requirement compared with other supply chain. For the food supply chain, it is the key to store the food. The research on the inventory forecasting of the food supply chain can estimate effectively the number of the food stocks. Then, it can reduce the loss of the retailers. In this paper, aiming at the problem of the local optimization and the convergence, we introduce the genetic algorithm to help to optimize the grey neural network prediction model. We use the model proposed in this paper to predict the inventory of food retailers. The results show that the RBF model which is optimized by the genetic algorithm has higher prediction accuracy and strong practicability.


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References


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