Vegetable Replenishment and Pricing Strategy Model Based on ARIMA Model and Particle Swarm Optimization Algorithm

In order to improve the profit of superstores, this paper investigates the optimal strategy of replenishment and pricing of superstore vegetable products. In this paper, we first analyze the distribution law of the sales of different single vegetables in different categories, use the Pearson correlation coefficient to analyze the relationship between different categories of vegetables, and use the Spearman correlation coefficient to portray the relationship between different single vegetables. Then, the weighted average method is used to find the average pricing of different categories of vegetables, and a linear regression model is set up by the least squares method. On the basis of the above regression equations, the ARIMA model is used to predict the future sales volume, and a single-objective planning model is set up, and a particle swarm optimization algorithm is used to determine the replenishment plan and pricing strategy for the week in the vegetable category. Finally, the objective function is set, the multi-objective planning model is constructed, and the original multi-objective planning model is transformed into a single-objective planning model by considering one of the objective functions as a constraint, and the non-linear planning method is used for solving the problem to determine the specific replenishment plan and pricing strategy for a single product on a single day. The proposed model has a certain degree of realism and high computational efficiency.