A study of vegetable replenishment and pricing based on nonlinear programming

Jinpeng Ye; Weipeng Ye; Gongwen  Liu; Xuejun Chen; Yinglin Huang

doi:10.54097/jm2r4t66

Authors

Jinpeng Ye
Weipeng Ye
Gongwen Liu
Xuejun Chen
Yinglin Huang

DOI:

https://doi.org/10.54097/jm2r4t66

Keywords:

Vegetable Replenishment and Pricing, ARIMA, Time Series, Nonlinear Programming.

Abstract

Vegetable replenishment and pricing are the key concerns of supermarket, and accurately predicting the future demand and pricing of vegetables in supermarket is of great significance for supermarket to improve their revenue. This paper predicts the mean and variance of the demand and pricing of each vegetable category in supermarket in the future based on the ARIMA time series and constructs a nonlinear planning model to maximize the revenue. The model yields the optimal demand and pricing for the supermarket for the next seven days, and the results show that the sales of all vegetable categories except edible fungi continue to rise. The results fit the historical situation and the predictions are accurate, which provides a reference basis for the replenishment and pricing of the supermarket.

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