Research on Pricing and Replenishment Strategies of Supermarket Vegetables Based on LSTM Neural Network and Goal Planning

Authors

  • Yishan Zhou

DOI:

https://doi.org/10.54097/jceim.v11i3.12

Keywords:

Vegetable Pricing, Correlation Analysis, LSTM Neural Network, Objective Planning

Abstract

This paper focuses on the daily replenishment volume and pricing of vegetable commodities, especially the impact of freshness on market selling price. Through Pearson correlation analysis, this study first explored the correlation between different vegetable categories and their single products and evaluated the sales correlation between each category and the single products of the same category. Secondly, this paper considers the cost-plus pricing method to analyze the relationship between the total sales volume of vegetable products and the pricing. The LSTM neural network model is used to forecast the sales volume and wholesale price of vegetable commodities, with special attention to the forecast data from July 1 to 7. Finally, this paper establishes a goal planning model with the goal of maximizing supermarket returns. Considering the total quantity of replenishment, sales volume and cost pricing as constraint conditions, the depth-first search algorithm is adopted to solve the problem, and the daily replenishment volume and pricing strategy for vegetable products in the next week are provided.

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Published

27-11-2023

Issue

Section

Articles

How to Cite

Zhou, Y. (2023). Research on Pricing and Replenishment Strategies of Supermarket Vegetables Based on LSTM Neural Network and Goal Planning. Journal of Computing and Electronic Information Management, 11(3), 51-56. https://doi.org/10.54097/jceim.v11i3.12

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