Rnn-Ade-Based Multi-Product Replenishment-Pricing Modeling Study
DOI:
https://doi.org/10.54097/j5b4q958Keywords:
RNN, Adaptive DE algorithm, Replenishment-Pricing Model.Abstract
Unscientific replenishment-pricing strategies can greatly reduce the sales profit of supermarkets, and choosing an appropriate decision model is crucial for supermarket operations. Therefore, this paper proposes a multi-product replenishment pricing model based on RNN-ADE, which fully utilizes the details of historical sales transactions with the help of recurrent neural networks to make the model results more credible, and also improves the DE algorithm to give the algorithm adaptability, so that the model has excellent global optimal solution search capability while having excellent solution efficiency. Then the model is tested practically based on the historical transaction flow detail data and historical product wholesale price of x superstore, and the replenishment and product pricing strategy for 2023-07-01 is formulated, the profit is predicted to be $741.44, and the sensitivity test is carried out. The results show that the model can effectively help supermarkets make more accurate and efficient replenishment and pricing decisions in a complex market environment.
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