Research on Active Pricing and Replenishment Decision Making of Vegetable Commodities Based on Particle Swarm Algorithm

Abstract

With economic development, people's eating habits are becoming more green and healthy, which provides a wide market for the sale of vegetable products. Due to the short shelf life and easily damaged characteristics of vegetable products, most of them cannot be sold the next day. As a result, supermarkets need to replenish their stock on a daily basis based on historical sales data. To help supermarkets formulate pricing and replenishment strategies to maximise sales, this paper takes two approaches based on domestic and international studies. First, this paper draws a scatter plot of total sales and markup rate and fits the functional relationship and concludes that the correlation between the two is not strong; it adopts a time series forecasting model to determine the cost price of each vegetable category for the next seven days, and solves the total daily replenishment and pricing for the next seven days using particle swarm algorithm through the establishment of a profit objective function. Second, considering the limited selling space of supermarkets and satisfying the market demand as much as possible, this paper introduces the constraint that the sellable single product categories are within 20, and solves for the replenishment and pricing of single products on 1 July 2023 in supermarkets. This paper provides workable ideas for pricing and replenishment decisions for vegetable products in supermarkets.