Research on automatic pricing and replenishment decision model based on vegetable products

Abstract

This paper studies and analyzes the pricing and replenishment decision-making problems in vegetable sales management, and discusses the relationship between the sales volume of various categories and single products of vegetables. After Morlet wavelet analysis, it is found that the sales volume changes in a seasonal cycle. This paper chooses Spearman correlation coefficient for correlation analysis. After using K-Means clustering, Apriori algorithm is used to mine association rules to complete the interpretation of the distribution law and mutual relationship of various categories and single product sales of vegetables. In view of the relationship between the total sales of vegetables and the cost-plus pricing, this paper finds that the distribution relationship between the total sales and the cost-plus pricing is exponential distribution. In addition, a variety of regression analysis algorithms are used for fitting, and the random forest model is selected as the most suitable model. At the same time, based on the seasonal ARIMA algorithm, the demand and cost price in the next week are predicted. In this paper, the genetic algorithm is used to solve the model, and finally the daily replenishment amount and pricing strategy of vegetable category in the next week are obtained.