International Journal of New Developments in Engineering and Society, 2024, 8(2); doi: 10.25236/IJNDES.2024.080206.
Zixin Zeng1, Yuhao Wan2, Yang Zhang1
1School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, 214122, China
2School of Internet of Things Engineering, Jiangnan University, Wuxi, 214122, China
Due to the short shelf life of vegetables, this article aims to provide supermarkets with automatic pricing and replenishment decision support for vegetable products, aiming to optimize category structure, increase profitability, reduce loss rates, and enhance service quality. To this end, we employed Spearman's correlation model, hierarchical clustering model, integer programming model, and LSTM time series model to conduct in-depth research on the purchase quantity and pricing strategies of vegetable products. The research results indicate that: 1) through correlation analysis, we can understand the relationship between the sales volume of various vegetables; 2) through linear regression fitting and the establishment of LSTM time series models, we can better predict daily sales volume and daily replenishment volume, thereby providing a basis for pricing decisions; 3) in the process of vegetable pricing, combining genetic algorithm optimization with integer programming models can make pricing decisions more reliable. Additionally, adopting different pricing strategies for different vegetables based on consumers' purchasing psychology can help increase vegetable sales.
Long Short-Term Memory (LSTM) model, Hierarchical Clustering, Integer Programming, Genetic Algorithm
Zixin Zeng, Yuhao Wan, Yang Zhang. Data Analysis and Pricing Model Exploration in Supermarket Vegetable Sales. International Journal of New Developments in Engineering and Society (2024) Vol.8, Issue 2: 37-43. https://doi.org/10.25236/IJNDES.2024.080206.
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