Research on Vegetable Pricing and Replenishment Strategy Based on TOPSIS Method and Simulated Annealing Algorithm

Abstract

By mining the relationship between categories and individual products, this paper puts forward enterprise replenishment and pricing strategies under different conditions. First of all, from the 49 kinds of vegetables sold, the TOPSIS method based on entropy weight is used to select the top 20 important items by taking the market demand and excess income as the first-level index, and taking the historical sales rate, average daily sales volume, variance of sales volume, vegetable loss rate and unit profit as the second-level index. Then, based on the concept of complementary and alternative goods, through the Spearman correlation coefficient, and using the weighted correlation coefficient method, select the top 9 items, such as ginger, garlic, millet pepper combination and so on. Using these selected items, taking the profit maximization of fresh merchants as the objective function and the demand of each category as constraints, a 0-1 integer nonlinear programming model is established. Then, the simulated annealing algorithm is used to simulate the planning for 1000000 times, and the pricing strategy and supply plan of the enterprise on July 1st are obtained, and the maximum profit on that day is 2098.17 yuan.