Research on Automatic Pricing and Replenishment Decision of Vegetable Products Based on Machine Learning
DOI:
https://doi.org/10.54097/jq42y878Keywords:
Machine Learning, Predictive Models, Dynamic Programming Models, Genetic Algorithms.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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References
Liu Yuchen. Research on the Joint Decision Problem of Dynamic Pricing and Inventory Control [D]. Shanghai Maritime University.
Zhao Yaya, Zhang Zeren, Dai Yongfu. Prediction of Agricultural Product Sales Range Based on Machine Learning [J]. The Computer Age, 2020(7): 4.
N. Ahmed Mahoto, R. Iftikhar, A. Shaikh, Y. Asiri, A. Alghamdi et al., "An intelligent business model for product price prediction using machine learning approach," Intelligent Automation & Soft Computing, vol. 30, no.1, pp. 147–159, 2021.
Sujjaviriyasup T, Pitiruek K. Agricultural product forecasting using machine learning approach. [J]. International Journal of Mathematical Analysis, 2013, 7(37): 1869-1875.
Chen Z, Goh H S, Sin K L, et al. Automated Agriculture Commodity Price Prediction System with Machine Learning Techniques [J]. 2021.
Nicoleta T, Stavros S, Manos R. Data-Driven Decision Making in Precision Agriculture: The Rise of Big Data in Agricultural Systems [J]. Journal of Agricultural & Food Information, 2019.
Wang Xinwu and Jin Peiyun. A Price Prediction Method for Agricultural Products in Longdong Based on Set Empirical Mode Decomposition and Autoregressive Neural Network [J]. Journal of Lanzhou Institute of Technology, 2022, 29(4): 85-88.
Xu Qigang. A Price Prediction Model for Agricultural Products in Shandong Province Based on Improved KNN-BPNN Algorithm [D]. Jinan University,2015.
Huang Yu, Chen Ting. Establishment of a BP neural network prediction model based on agricultural product price prediction [J]. New Business Weekly,2018.
Kong Congcong. Research and implementation of agricultural product price prediction technology based on recurrent neural network [D]. University of Chinese Academy of Sciences.
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