Vegetable Automated Pricing Model Based on Particle Swarm Algorithm

Abstract

Fresh grocery supermarkets face challenges such as the short shelf life of vegetables, quality degradation over time, and difficulty in selling products the following day. Considering these issues, they restock early in the morning without complete knowledge of the specific items or purchase prices, based on historical sales and demand data. To address this issue, this paper first visualizes the data to demonstrate a positive correlation between sales volume and time. Next, to address the issue of predicting the total daily restocking quantity and pricing strategy from July 1 to July 7, 2023, the document utilizes data from the previous three years to establish an ARIMA time series forecast model to predict the costs and sales volume of six vegetables for the first week of July 2023. Finally, to devise the replenishment quantity and pricing strategy for individual items on July 1, 2023, the paper filters the data for all vegetable items from June 24th to 30th, 2023, and combines it with the particle swarm optimization algorithm to obtain the results. This study is pragmatic, providing a feasible solution to the problem of automatic pricing for fresh vegetables.