LR-GA Algorithm Based Study on Vegetable Replenishment and Pricing Decision Making

Abstract

The sales volume and sales price of vegetable products fluctuate greatly due to factors such as origin, variety and freshness period. Therefore, rational replenishment and pricing decisions are particularly important for supermarkets. Therefore, this paper proposes a replenishment and pricing strategy model for vegetable products based on linear regression-genetic algorithm. In this paper, all vegetable commodities are first classified into four categories using K-mean cluster analysis, and the sales performance of the four categories is observed to understand their sales behaviors and provide references for sales strategies. Then, the cost, price and profit margin of each vegetable commodity are calculated using the weighted method, and the linear regression equation between the sales volume of vegetable commodities and the weighted sales price is given. Finally, using the total profit formula considering the loss rate of each item as the objective function and the linear regression equation between the total sales volume of each vegetable item and the weighted sales price as the constraints, the optimization search is carried out by using linear regression and Genetic Algorithm (LR-GA) to find out the sales price and the sales volume under the maximum profit so as to realize the sales strategy.