Research on Pricing and Replenishment Strategy for Vegetable Commodities based on Non-linear Programming

Abstract

With the improvement of people's consumption level and the change in consumer mindset, how to develop pricing and replenishment strategies has become an urgent issue for fresh food supermarkets. This paper first utilizes an improved ARIMA-LSTM model to forecast the purchase price and sales volume of six types of vegetables for the next 7 days. It then conducts regression analysis on the sales volume and cost-plus pricing using GBoost algorithm. Furthermore, a Non-linear Programming model for maximizing profits is established, and this article solves it based on optimizing Genetic Algorithm and obtains a pricing and replenishment strategy finally. The strategy shows that the pricing of "Edible mushrooms" is the highest and the daily replenishment quantity of "Leafy greens" is the largest. The maximum profit over seven days is 6978 yuan. Overall, this study contributes to optimizing sales and supply chain management, achieving better profitability and customer satisfaction.