Pricing and Replenishment of Vegetable Commodities Based on Machine Learning and Operations Research

To enhance replenishment and pricing decisions for fresh vegetable products in fresh food supermarkets, this study develops machine learning time series forecasting and operations research models.By examining the historical sales and demand patterns, we investigate the relationships between different categories of vegetables and their sales volumes.We then investigate optimal replenishment and pricing strategies under different demand and supply scenarios.The sales unit price, category name, wholesale price, and loss rate were linked via product coding for data preprocessing, showing no missing values.The sales volumes of six vegetable categories were analyzed using the Kruskal-Wallis non-parametric test, followed by a differential analysis.The results indicated a significant difference between sales volume and category code.Finally, an XGBoost model was established to explore the impact of relevant features on sales volume.This study utilized the magnitude of the R-squared values in SPSS software to assess the goodness of fit and choose suitable models.Subsequently, a replenishment plan for supermarkets was established.Experimental forecasting and demand forecasting were conducted using ARIMA time series analysis to determine the total daily replenishment quantity for the upcoming seven days.