Intelligent Optimization Analysis of Automatic Pricing and Replenishment of Vegetable Products Based on Combinatorial Machine Learning Models

Based on the idea of market demand and supply, this paper establishes a combined machine learning model by determining the indicators such as sales volume, demand, supply, etc., with the goal of maximizing the interests of the superstore, and adopts an intelligent optimization algorithm to realize the optimization analysis of automatic fixing and replenishment for vegetable commodities. First, Spearman correlation coefficient is used to quantitatively analyze the correlation between different categories to quantify the degree of correlation between different vegetable categories, and K-means cluster analysis is used to analyze the sales data of each category of vegetables, and different vegetables are divided into different clusters based on sales volume, so as to identify the vegetable items with similar sales volume. Then, XGBoost model and time series autoregressive model were used to construct a combined machine learning model, which realized the prediction of future price data based on historical price data and the prediction of future sales volume by combining the unit price and the price, and carried out the smoothing test and the seasonality test on the data to further optimize the predictive ability of the model. And it realized the prediction of future week's price, and the prediction of future week's price was realized based on the prediction results of the combined machine learning model. Based on the prediction results of the model, an automatic pricing strategy and replenishment strategy function with the goal of maximizing revenue is established, and genetic algorithm is applied to realize the solution of the model to obtain the corresponding optimal pricing. Finally, the objective function is shifted from the overall category to specific commodities, and the constraints adapted to it are set for specific time points, which are still solved by intelligent optimization algorithms and optimization analyses are carried out, and specific replenishment quantities. And pricing strategies are formulated for the commodities, so as to achieve the maximum revenue of the superstore.