Replenishment and Pricing Decision Model for Vegetables
Pages 692 - 697
Abstract
In fresh food supermarkets, vegetable commodities have a short shelf life and their quality deteriorates with the increase of selling time. Therefore, there is a situation that the variety, quantity, and price of vegetable commodities vary from day to day, and the superstores need to analyze the market demand reliably and set the daily selling price by using the "cost-plus pricing" method in order to obtain the optimal profitability. This paper discusses the optimization problem of daily replenishment and pricing decision of supermarkets. This paper firstly analyzes the regularity and correlation of vegetable commodity data based on Spearman's correlation analysis, then establishes the linear regression equations of sales volume, cost, and profit through ridge regression and carries out the R2 test, secondly, it establishes a convolutional neural network prediction model by using the historical data of sales volume and wholesale price to predict the future week's sales volume and price, and finally, it uses the improved simulated annealing algorithm to compute the maximum return that matches the actual number of vegetable items demanded.The maximum gain on July 1, 2023 is 881.50 yuan.
References
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Index Terms
- Replenishment and Pricing Decision Model for Vegetables
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Published In
December 2023
917 pages
ISBN:9798400716669
DOI:10.1145/3659211
Copyright © 2023 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 29 May 2024
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BDEIM 2023
BDEIM 2023: 2023 4th International Conference on Big Data Economy and Information Management
December 8 - 10, 2023
Zhengzhou, China
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