Vegetable pricing and replenishment strategies are crucial issues in the retail industry. Globally, as populations grow and market competition intensifies, research into how to determine prices and optimize replenishment strategies is increasingly critical. In this article, we focus on pricing and replenishment strategies for fresh vegetables. First, this paper uses the FP-growth algorithm to calculate multiple vegetable combinations that are often purchased together. Then, to explore the relationship between sales volume and cost-based mark-up pricing, we used the ARIMA model to predict and then used linear regression to derive a fitting regression equation for the relationship between overall sales and price. Finally, the particle swarm optimization algorithm is employed to maximize total profit through optimal price and replenishment strategies. Based on this result, the final pricing strategy is formulated and the corresponding replenishment strategy is formulated. The significance of our research on this project is to improve the accuracy and efficiency of price and replenishment decisions through optimization algorithms, thereby maximizing profits and improving the effectiveness of supply chain management. This research contributes to the ongoing development of data-driven decision-making models in the retail industry, particularly in the area of perishable goods management.
Wang et al. (Mon,) studied this question.