Research on vegetable pricing and replenishment strategy based on genetic algorithm

Abstract

In this paper, the average sales volume and cost plus pricing of 6 categories of vegetables are calculated. The regression analysis is carried out by using R language. Then, with profit maximization as the objective function and annual sales volume, replenishment volume, unit price of sales and loss rate as the constraint conditions, a quadratic programming model is established. The total replenishment of flowers and leaves, cauliflower, aquatic rhizome, capsicum, nightshade and edible fungi from July 1 to 7, 2023 was 111.27, 22.82, 21.24, 46.88, 14.23 and 40.40 kg, respectively. The expected pricing is 10.28, 12.92, 11.77, 20.56, 12.96 and 11.91 yuan/kg, with a total profit of 1843.8 yuan. On this basis, with the number of orders and sales pricing of merchantable items as decision variables, an integer programming model was established according to the constraints such as the number of items, minimum display quantity, price range and loss rate, etc. Genetic algorithm was used to solve the integer programming model, and the replenishment volume and pricing strategy of 35 items were obtained on July 1, with an estimated profit of 2398.2 yuan.