Optimization Of Vegetable Sales Strategies and Daily Replenishment in Supermarkets
DOI:
https://doi.org/10.54097/bw62a155Keywords:
Polynomial Regression, LSTM, Nonlinear Programming.Abstract
Vegetable products in the superstore have a shorter freshness period, the quality will deteriorate with the increase of sales time, and most varieties can only be sold on the same day. In order to develop a reasonable pricing and replenishment program, this paper first establishes a polynomial regression model to determine the relationship between the total sales volume of each vegetable category and the cost-plus pricing, taking the daily pricing of each category as the independent variable, the daily sales volume of each category as the dependent variable, and taking the polynomial order as 3 to construct the regression function. Then the LSTM model is constructed to predict the confidence interval of the total sales volume of each category in the next seven days, and finally this paper constructs a nonlinear programming model to maximize the revenue of the day as the objective to construct the revenue function, with the constraint that the total daily replenishment volume of each category needs to satisfy the confidence interval of the total sales volume, and then the simulated annealing algorithm is used to find the optimal total daily replenishment volume and pricing strategy in the next seven days.
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