Yuzhe Huang
ABSTRACT
Smart pricing and replenishment decisions for goods are very important decisions for retailers and manufacturers in their operations. These decisions have a direct impact on the profitability and market competitiveness of the enterprise. Intelligent commodity pricing refers to the process of automatically adjusting commodity prices according to market demand and competition, using intelligent algorithms and data analysis technology. This pricing method can help businesses better understand the market, grasp customer needs, and increase sales and profit margins. With intelligent commodity pricing, businesses can quickly respond to market changes, optimize pricing strategies, and increase sales volume and profitability. First of all, this paper has fully preprocessed the data, and after the data processing, we have made detailed statistics on the total sales of the six major vegetable categories, summarized the sales data by week and month, and counted the sales of each single product and category respectively. Secondly, the data were visually analyzed, including JB normal distribution test, and the correlation analysis of the top 10 selling items was carried out, and the correlation of sales of vegetable categories was also studied in depth. From these analyses, we came to the conclusion of the correlation between the various categories of vegetables. A variety of machine learning models are used to construct the relationship between the wholesale price, sales unit price and sales volume of each category of vegetables, and the optimal regression model is selected through the evaluation index. Then, the sales price of each category is greater than the wholesale price as the constraint to construct a dynamic programming model. Finally, in order to obtain the global optimal solution, this paper intends to use the particle swarm optimization algorithm to obtain the maximum revenue of the supermarket in the next seven days of 50571.263 yuan.
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Index Terms
- Commodity intelligent pricing and replenishment decision based on dynamic programming model based on sliding window and XGBoost regression algorithm
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