Pricing and Replenishment Strategy Modeling of Superstore Vegetable Commodities based on LSTM and HTSO

In the fresh supermarket, the sales price of vegetable products will change due to the influence of preservation period and product equivalence factors, and the sales volume will change with the influencing factors, so the supermarket needs to adopt different replenishment and pricing decisions. In this paper, improved tuna swarm search algorithm (HTSO), Kendall correlation, and gray correlation model are used for in-depth study and formulation of pricing and replenishment decisions for vegetable category items. Firstly, Kendall correlation analysis and gray correlation analysis are used to evaluate the interrelationships between and individual items. Finally the HTSO algorithm is utilized to solve to get the pricing and replenishment strategy for the next 7 days. The conclusion is that holidays, seasons, vegetable supply chain and consumer group indicators will have a certain impact on the vegetable sales of the supermarket to some extent, and these indicators will also affect the pricing and replenishment strategy of the supermarket for vegetable products.