Vegetable sales forecasting based on nonlinear programming model

Abstract

The shelf life of vetgetable products is relatively short,and the product deteriorates with the increase of sales with time,and most varieties are not sold on the same day.Today’s domestic offine retail are under greater pressure,losses are mainly affected by changes in the fair value of financial assets,the Coronavirus and other factors.Reliable market demand analysis is especially important for replenishment and pricing decisions.Reliable market demand analysis is especially important for replenishment and pricing decisions.In order to improve the revenue of supermarkets, this paper use the historical sales volume and price of vegetables in supermarkets,uses the systematic clustering method to analyze the relationship between the totla sales volume of each vegetable category and the cost-plus pricing with ARIMA prediction model Markov chain deepening nonlinear programming model ,and gives the total daily replenishment and pricing strategy to maximize the profit of supermarket.The results of the study predicted the sales of various vegetables,and concluded that people tend to buy edible fungi and aquatic root products.