Abstract
In fresh food supermarkets, due to the short shelf life of vegetable products and the deterioration of products over time, unsold products on the same day cannot be sold the next day. Therefore, supermarkets need to make automatic pricing and replenishment decisions on a daily basis based on historical sales and demand. All product data for this study were provided by the 2023 Higher Education Society Cup National College Student Mathematical Modeling Competition.This study aims to explore the relationship between commodity sales and wholesale prices and pricing using five machine learning methods: neural networks, linear regression, decision tree regression, random forest regression, and XGBOOST regression, in order to predict sales and wholesale prices for the next week. By comparing the performance of five machine learning methods, it was found that the XGBOOST regression prediction model is the most suitable for predicting the daily sales of goods. The linear regression prediction model is the most accurate for predicting the wholesale prices of "chili", "flower and leaf", "edible fungi", "cauliflower", and "eggplant" goods, while the random forest regression prediction model is the most accurate for predicting the wholesale prices of "aquatic rhizome" goods. By constructing a dynamic programming model and using genetic algorithms to solve, automatic pricing and replenishment decisions for various goods within the next week can be obtained.
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