Vegetable Pricing and Replenishment Decision Based on Correlation Analysis and Entropy Weight-TOPSIS Model

Abstract

This article studies the automatic pricing and replenishment decision-making of vegetable products through statistical analysis and modeling. Firstly, a descriptive statistical analysis of the data was conducted, and it was found that the vegetable categories did not fully conform to the normal distribution relationship. The correlation between each category was also explored. Subsequently, grey correlation analysis was used to explore the correlation between individual products within each category. Then, in the overall modeling process, the entropy weight TOPSIS model is used to select sellable items, and linear regression is used to predict the pricing situation for the next day. Using ant colony algorithm to solve the maximum return portfolio pricing strategy model, a profit of 912.235 yuan and corresponding pricing strategy and replenishment volume were obtained. Finally, five factors that affect the profits of supermarkets were proposed, and relevant suggestions were given to enhance the profits of supermarkets.