A Study on Autonomous Pricing and Replenishment Decision of Vegetable Commodities Based on Gray Time Series Forecasting and Single-Objective Planning Models

Abstract

Automated pricing and replenishment of vegetable items is one of the hot issues of the new era. In order to make replenishment decisions on the day's vegetable goods for superstores, this paper first constructs a nonlinear regression model to fit the function of vegetable category pricing and daily sales volume, and finds that the two have a negative correlation; Second, this paper predicts the cost of each category of vegetables for the coming week by building a gray time series prediction model; Finally, this paper takes the pricing of different categories on different dates as the decision variable, maximizes the revenue of the superstore as the objective function, establishes a single-objective planning model with the maximum capacity of the category and other constraints, and determines the optimal daily replenishment and pricing decisions based on the dynamic planning algorithm using the idea of iteration.