  With the expansion of operational scale of supermarkets in China, the
vegetable market has grown considerably. The decision-making related to
procurement costs and allocation quantities of vegetables has become a pivotal
factor in determining the profitability of supermarkets. This paper analyzes
the relationship between pricing and allocation faced by supermarkets in
vegetable operations. Optimization algorithms are employed to determine
replenishment and pricing strategies. Linear regression is utilized to model
the historical data of various products, establishing the relationship between
sale prices and sales volumes for 61 products. By integrating historical data
on vegetable costs with time information based on the 24 solar terms, a cost
prediction model is trained using TCN-Attention. The Topis evaluation model
identifies the 32 most market-demanded products. A genetic algorithm is then
used to search for the globally optimized vegetable product allocation-pricing
decision.
