Vegetable replenishment and pricing model for supermarkets based on BP neural network and planning model

Vegetable goods in the superstore have high freshness requirements, in order to make the maximum profit under the condition of meeting the market demand, it is necessary to determine the total amount of replenishment as well as the pricing strategy. This paper applies BP neural network to establish a model to get the relationship between vegetable sales and cost-plus pricing. Finally, the objective function is constructed with the maximum profit, and a planning model is established to determine the total amount of replenishment in the coming week and formulate the pricing strategy with the maximum profit. It aims to solve practical problems and help superstores better determine the replenishment volume and pricing strategy of vegetables.