Research on Vegetable Commodity Pricing and Replenishment Strategy Based on Simulated Annealing Algorithm

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In fresh food superstores, the freshness period of most vegetable products is relatively short, and the quality of vegetables will deteriorate due to the increase in storage time and cannot be sold on the next day, so it is very important to determine the total daily replenishment for superstores. This paper discusses the optimization problem of daily replenishment total and pricing decisions. In this paper, firstly, the bivariate correlation coefficient analysis is carried out using SPSS to calculate the correlation coefficients between the total sales volume of each category and the unit price of sales; secondly, the regression model is established to derive the fitting equations and the R2 the test is carried out. For the superstore revenue optimization problem, firstly, the total daily replenishment strategy model of each vegetable category was established, and secondly, the pricing strategy model of each vegetable category was established through the wholesale price and sales unit price. Considering the transportation loss and deterioration of vegetables, the goods will be sold at a discount, this paper establishes the pricing strategy model of undiscounted and discounted sales. Finally, the optimal model is obtained based on the simulated annealing algorithm to ensure revenue maximization. The final maximum returns (RMB) from July 1–7, 2023 are 8091.56, 8102.48, 8097.47, 8114.66, 8166.51, 8115.96, and 8126.11, respectively.

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