Due to the nature of fresh commodities with short shelf life and strong seasonality, this paper is based on the study of the correlation between different vegetable categories and the relationship between sales volume and time, relying on historical sales data and using a variety of analytical methods to establish an optimization model of replenishment strategy and pricing strategy for fresh superstores. Firstly, the changes in the sales volume of goods in different categories with season or time are analyzed, the correlation between categories is quantified through Pearson correlation analysis to determine the purchase relevance of consumers between different categories, and the single product under the same category is analyzed by using systematic clustering method. Then, a one-dimensional linear regression model is established, and the maximum demand under the premise of satisfying the balance of supply and demand is used to determine the supply through the time series forecasting model, and one week is taken as the forecasting cycle, and the replenishment of each type of goods in the coming week is predicted by using Winters multiplier method and ARIMA(p,d,q) model. Finally, the cost-plus pricing is analyzed to determine the single-item pricing optimization model, and the multi-objective particle swarm optimization algorithm is used to analyze and solve the pricing model for single-day replenishment strategy. The pricing model proposed in this paper can reasonably help the market to realize the balance of supply and demand, promote fair competition in the market, improve the market structure, and then realize the sustainable development of the economy. The model can be further generalized to other similar studies to help the pricing situation of other commodities in life.

Yiwen Liu, Miaoru Li, Yifan Pu. Replenishment and Pricing Strategies for Vegetable Commodities Based on Optimization Class Models. Academic Journal of Business & Management (2023) Vol. 5, Issue 26: 164-171. https://doi.org/10.25236/AJBM.2023.052625.

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