Genetic Algorithm Based Optimization Solution for Supermarket Replenishment and Pricing Strategies

The vegetable commodities sold by fresh merchants have short freshness and decrease with time, and need to be replenished and pricing decisions every day. the optimization of replenishment and pricing strategy can effectively improve the efficiency of commercial surplus, meet the market demand, and achieve sustainable operation. in this paper, in order to analyze the relationship between the total sales volume of each vegetable category and the cost plus pricing, and to predict the pricing strategy and the total replenishment volume in the next week, firstly, the relationship charts of sales volume, unit cost, and profit rate of six categories are fitted respectively. after goodness of fit evaluation and analysis, the fitting effect of most categories is better, and the fitting function expressions of six categories are given. then, a linear programming model was constructed with the goal of maximizing supermarket revenue and the net sales volume, pricing, and unit cost of each vegetable as decision variables. for the maximum sales volume constraint in the constraint conditions, the sarima algorithm was used to forecast the vegetable demand based on seasonal test and periodic effect test, and the predicted value was taken as the maximum sales constraint. finally, the solution is based on a genetic algorithm. the results show that the profit rate of pepper is the largest, the profit rate is the lowest, and the more stable flowering vegetables.