Automatic Pricing and Replenishment Decision-Making for Vegetable Commodities Based on Bi-directional Long Short-Term Memory Recurrent Neural Networks and Markov Prediction Models

Abstract

 Reasonable pricing and replenishment strategies are crucial for vegetable superstores to maximise profitability. In this paper, we firstly analyse the distribution pattern and interrelationship of each category of vegetables through Pearson correlation coefficient, and then use MATLAB tools to analyse and predict the data through the time series analysis of Long Short-Term Memory Recurrent Neural Networks (LSTM), and solve the optimal daily replenishment and pricing of each category in the coming week; lastly, we firstly use the small-period sample prediction method, i.e., the Markov prediction model, to analyse the data, and then construct a multi-objective planning model to further determine the optimal pricing strategy. Finally, we use the small-period sample prediction method, i.e., Markov prediction model, to analyse the data and then construct a multi-objective planning model to further determine the optimal pricing strategy.