Research on Vegetable Commodity Pricing and Replenishment based on Planning Models and Genetic Algorithm

Abstract

This paper focuses on an in-depth study of supermarket vegetable pricing and replenishment problems, utilizing a variety of methods such as statistics, prediction models, planning models and other methods of analysis. First, the data were preprocessed, and frequency distribution histograms were drawn, revealing the distribution pattern and correlation between each category and each single product of vegetables through descriptive statistical analysis. Secondly, for the relationship between sales and pricing, the sales unit price was averaged through the cost-plus pricing formula, and MATLAB was used to nonlinearly fit the relationship between the total sales volume of the categories and the sales price, and the fitting result was further optimized through neural network, and a nonlinear planning model was established, and a genetic algorithm was used to solve the daily replenishment volume of supermarkets and the pricing strategy in order to achieve the maximization of revenue. Finally, the top-rated individual products were screened out by entropy weighting method, and a linear programming model was established to predict the replenishment quantity and pricing strategy for the coming day, which further provided effective decision support for the sales management of the supermarket.