Research on automatic vegetable pricing and replenishment decision-making based on improved ant colony algorithm

Zihao Wang; Kehang Mu; Yuhan Ma

doi:10.54097/hset.v70i.13912

Authors

Zihao Wang
Kehang Mu
Yuhan Ma

DOI:

https://doi.org/10.54097/hset.v70i.13912

Keywords:

Ant colony algorithm, ARIMA prediction model, Automatic replenishment and pricing.

Abstract

In daily life, vegetable products have a short shelf life and a high perishability rate. As time goes by, their quality will deteriorate and cause waste. Therefore, in order to maximize benefits and reduce waste, the ant colony algorithm and ARIMA are combined The prediction model was combined and optimized, and the Ant Colony-ARIMA prediction model was established, and the relevant data of the sales flow details and wholesale prices of each commodity in a supermarket from July 1, 2020 to June 30, 2023 were used for training, optimization and prediction. Analyze and compare with various data from June 24, 2023 to June 30, 2023. The results show that the average relative errors of sales and pricing of the Ant Colony-ARIMA model are lower than the actual ones, indicating that the established Ant Colony-ARIMA model can accurately predict sales and pricing, and provide a high-quality solution for supermarkets and merchants. Accurate prediction method.

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