Research on Vegetable Pricing and Replenishment Model Based on Greedy Algorithm

This article analyzes the pricing and replenishment decisions of supermarkets, exploring the distribution patterns and correlations between different vegetable categories and individual products. On the other hand, a robust linear programming model is constructed, which can iteratively optimize the corresponding replenishment plan, significantly improving the profitability of supermarkets. This provides important practical significance for the operation and sales of vegetable products in fresh food supermarkets. Through Spearman correlation coefficient analysis, it was found that there is a close relationship between cauliflower and leafy vegetables. The correlation between various vegetable items was processed using K-means clustering method, and the correlation between each item was discussed after classifying the groups. Finally, the grey correlation analysis method was used to further explore the ranking of the correlation between each category and individual product.