Vegetable Purchase and Pricing Strategy Combined with ARIMA, K-means, LSTM and Optimal Linear Programming

Ziwei Li

doi:10.6981/FEM.202511_6(11).0019

Authors

Ziwei Li

DOI:

https://doi.org/10.6981/FEM.202511_6(11).0019

Keywords:

Linear Regression Analysis; Optimal Linear Programming; ARIMA Model; K-means Clustering Analysis; LSTM Model.

Abstract

The shelf life of vegetables is short, and with the serious loss of time, it is important for supermarkets to make reasonable replenishment decisions and pricing. Based on the quantity control of single items and the minimum display quantity of each single item, this paper constructs an optimal linear programming model with the ultimate goal of maximizing the benefits of supermarkets and meeting the market demand. Based on ARIMA model, K-means clustering analysis, LSTM model training validation, brought into linear regression analysis to establish a prediction model. It is expected that the compound model will be widely used in supermarkets to make purchase decisions and pricing decisions.

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References

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