Optimization of replenishment and sales strategy of vegetable products based on ARIMA time series model and combination sales

Abstract

Due to the short shelf life of vegetable items, supermarkets usually restock daily based on the historical sales and demand of each item. In order to develop an optimal strategy for replenishment and sales of vegetable items, this study firstly predicted the total daily replenishment in the coming week using ARIMA time series model, and then this study took the hypermarket revenue as the objective function and considered the correlation between vegetable categories in the sales data, and adopted a combination sales strategy to increase the sales volume. This study establishes the combination sales formula, and uses the third-party library function combined with the genetic algorithm to obtain the optimal coefficient combination to maximize the superstore's revenue, and finally constructs the optimization model of pricing strategy for each category of vegetables, and solves the optimal pricing strategy, and ultimately obtains the daily replenishment of total amount of various categories of vegetables in the coming week.