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This paper focuses on the pricing and replenishment decision problems for vegetable products in supermarkets. Pricing and replenishment decisions need to study the interconnections between categories and individual products, so as to make reasonable sales combinations; it also needs to study the correlation between sales volume and time, and predict the future purchase price and sales volume, through visual analysis and correlation analysis, multiple regression model, reveals the interconnections between categories and individual products, and combines the correlation between sales volume and time to predict the future price and sales volume, and developed a profit-maximizing replenishment pricing strategy. For each vegetable category, a multi-objective planning mathematical model was developed using multiple regression and time series models to determine the optimal total daily replenishment and time-of-day pricing strategies. At the individual item level, the entropy weight TOPSIS model was used to comprehensively evaluate and rank the individual items, and the top 33 individual items were selected as the target for stocking, and a multi-objective model was further established to develop the replenishment quantity and pricing strategy for July 1, 2023 for individual items. By focusing on verifying the impact of market price trends, this paper conducts a time series forecasting analysis by analyzing the relevant sales data of a large superstore in order to comprehensively optimize the superstore's vegetable selling strategy.
J. Qian (Tue,) studied this question.