Optimization decision model of vegetable stock and pricing based on TCN-Attention and genetic algorithm

Abstract: 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.