Automatic Pricing and Replenishment Strategies for Vegetable Products Based on Data Analysis and Nonlinear Programming

In the field of fresh produce retail, vegetables generally have a relatively limited shelf life, and their quality deteriorates with time. Most vegetable varieties, if not sold on the day of delivery, become difficult to sell the following day. Therefore, retailers usually perform daily quantitative replenishment based on historical sales data and demand conditions. Vegetable pricing typically uses a "cost-plus pricing" method, with retailers often discounting products affected by transportation loss and quality decline. In this context, reliable market demand analysis is crucial as it directly impacts replenishment and pricing decisions. Given the limited retail space, a rational sales mix becomes essential. This paper first uses data analysis and visualization techniques to examine the distribution patterns and interrelationships of vegetable sales quantities by category and individual item, based on provided data on vegetable types, sales records, wholesale prices, and recent loss rates. Next, it constructs a functional relationship between total sales volume and cost-plus pricing for vegetable categories, forecasts future wholesale prices using the ARIMA model, and establishes a sales profit function and constraints. A nonlinear programming model is then developed and solved to provide daily replenishment quantities and pricing strategies for each vegetable category for the upcoming week. Further, we optimize the profit function and constraints based on the actual sales conditions and requirements, providing replenishment quantities and pricing strategies for individual items on July 1 to maximize retail profit. Finally, to better formulate replenishment and pricing decisions for vegetable products, we discuss and forecast the data that retailers need to collect and analyses how the collected data can be applied to the above issues.