Author(s)

Xuanyi Jin ¹, Xinhong Liu ¹, Yanqi Zhang ¹, Ruisheng Zhang ¹, Nana Zhao ¹

Affiliation(s)

¹ Beijing Institute of Petrochemical Technology, Beijing, 102617, China

Corresponding Author

Xinhong Liu

ABSTRACT

Due to the short shelf life of vegetable products, supermarkets usually make daily replenishment plans based on historical sales data and demand for various categories of vegetables. Therefore, automatic pricing and optimization of replenishment strategies for vegetable products have become particularly important. The proportion and seasonal trend of each variety have been analyzed based on the total sales of each vegetable category in this article. We established a time series model for the daily average prices of various vegetables, used exponential smoothing to predict wholesale prices for next week, and finally listed the objective function and related constraints for supermarket returns. We optimized the model solution using genetic algorithm. In addition, weather data, customer purchasing habits and preferences data, supply chain data, and other data can be supplemented to help supermarkets better formulate replenishment plans and adjust pricing strategies, achieving maximum economic benefits.

KEYWORDS

Optimization model, genetic algorithm, time series, exponential smoothing model

CITE THIS PAPER

Xuanyi Jin, Xinhong Liu, Yanqi Zhang, Ruisheng Zhang, Nana Zhao, Research on Automatic Pricing and Replenishment Decision of Vegetable Products Based on Optimization Models. Financial Engineering and Risk Management (2024) Vol. 7: 84-91. DOI: http://dx.doi.org/10.23977/ferm.2024.070311.

REFERENCES

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