Research on Vegetable Pricing and Replenishment Strategies Based on Time Series and Linear Programming Models

Authors

  • RuiBing Wang
  • ZhiYi Niu
  • HuiKang Gao

DOI:

https://doi.org/10.54097/

Keywords:

Time Series Model, Linear Programming, Linear Regression Model

Abstract

 In this paper, an optimization model for pricing and replenishment of vegetable commodities was established based on historical sales data, wholesale price and wastage rate using linear regression, time series forecasting and linear programming. The model is analyzed and solved by Python, MATLAB, SPSS and other software, and the optimization strategy for commodity pricing and replenishment is proposed. First, bar charts and line graphs of sales volume changes were plotted using the matplotlib library in Python, and heat maps of correlation coefficients were plotted by the Pearson correlation coefficient method. Secondly, the relationship between sales volume and pricing is analyzed by scatter plot and linear regression model, and the time series model is constructed by using SPSS for sales volume forecasting. Finally, the restocking volume and pricing strategy for the vegetable category in the coming week are given based on the linear programming model to maximize the superstore revenue.

References

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Published

28-06-2024

Issue

Section

Articles

How to Cite

Wang, R., Niu, Z., & Gao, H. (2024). Research on Vegetable Pricing and Replenishment Strategies Based on Time Series and Linear Programming Models. Journal of Computing and Electronic Information Management, 13(2), 47-51. https://doi.org/10.54097/

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