Research and Analysis of Vegetable Pricing and Replenishment Decisions

Abstract

This paper uses LSTM to conduct a prediction study on the PS dataset to establish a revenue maximization optimization model by limiting the actual cost of a category that fluctuates within 10% of two adjacent days, using seven-day revenue as the objective function, and using the actual daily supplemental cost of each category as the decision variable. Taking the unit cost profit of a category as a category, the ratio of seven-day total profit to seven-day total supplemental cost, and based on cost-plus pricing, the cost price of an individual product for the next seven days can be estimated based on the unit cost profit of the corresponding category, so as to further estimate the pricing as the basis for pricing decisions. The Monte Carlo method is used to optimize the optimization objectives within different profit fluctuation percentage intervals using genetic algorithm, with a view to providing some implications for forecasting research in other fields.