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International Journal of New Developments in Engineering and Society, 2024, 8(2); doi: 10.25236/IJNDES.2024.080206.

Data Analysis and Pricing Model Exploration in Supermarket Vegetable Sales


Zixin Zeng1, Yuhao Wan2, Yang Zhang1

Corresponding Author:
Zixin Zeng

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

Cite This Paper

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.


[1] Huang He. Research on automatic commodity pricing model based on deep learning[J]. Modern Commerce Industry, 2019, 40(09): 188-190.

[2] Liu Hebing, Han Jingjing, Zhong Chenhui, et al. Research on vegetable price prediction model based on multi-scale feature fusion[J]. Journal of Henan Agricultural University, 2022, 56(5): 858-867. 

[3] Alabdallah A, Ohlsson M, Pashami S, et al.The Concordance Index decomposition -- A measure for a deeper understanding of survival prediction models[J].  2022.DOI:10.48550/arXiv.2203.00144.

[4] Chen Liping, Xing Xiaodan, Zhang Yuting, et al. Research on Consumer Behavior in Online Shopping [J]. E-commerce Research, 2023, 14(4): 56-62. (in Chinese) 

[5] Bekele R, Mcpherson M. A Bayesian performance prediction model for mathematics education: A prototypical approach for effective group composition[J].British Journal of Educational Technology, 2011, 42(3):395-416. DOI:10.1111/j.1467-8535.2009.01042.x.

[6] Lin Meina. Research on Sales forecasting based on multi-level time series model [D]. Jinan university, 2022. 

[7] Ji Y N. Research on supply chain pricing strategy of fresh e-commerce under the background of online shopping promotion [J]. Logistics technology, 2022, (02): 140-144. 

[8] Zhang Yan, Mou Jinjin, Wang Shuyun. Business super have some samples with price control supply chain optimization decision [J]. Journal of China management science, 2023, 31 (10): 266-275.