Research on Supermarket Vegetable Sales Optimization Based on Grey Prediction Model and Differential Evolution Algorithm

This paper mainly focuses on the comprehensive research of vegetable sales data in a supermarket, using statistical analysis, predictive modeling, and optimization algorithms to explore the distribution of vegetable sales, pricing strategies, and replenishment planning. Firstly, by analyzing the category sales data of different years, it is found that holidays have a significant impact on vegetable sales, and the grey correlation between each category is strong. Secondly, based on the cost-plus pricing theory, the ARIMA model is used to predict future sales, and the replenishment quantity and sales pricing of each category are determined by single-objective programming. Finally, considering the actual constraints, the grey prediction model and improved differential evolution algorithm are used to optimize the purchase quantity and pricing strategy of single item. The study of this paper provides scientific decision support for supermarket vegetable sales and helps to improve sales efficiency and economic benefits.