Computerized Intelligent Pricing and Replenishment Strategy for Vegetable Commodities Based on Dynamic Planning and Time Series Algorithm

Vegetable products' freshness and nutritional value, as an essential part of daily life, however, will continue to decline with the passage of time due to perishability. How to develop a scientific pricing and replenishment strategy is very important. In this paper, DBSCAN clustering model and time series algorithm are used to generate the sales distribution law of vegetable products in different seasons, and the correlation relationship of different categories is analyzed through Spearman and Pearson correlation coefficient detection. Then, based on the degree of change in freshness of each category of vegetables with the freshness of different times of the week with the rate of depletion, the time nodes are delineated to classify the sales cycle of vegetable products into the freshness period and the period of decline in freshness. A dynamic replenishment-pricing model based on customers' purchasing preferences (DRPM-CPP) is established with the goal of maximizing the profit value of supermarkets. Finally, an improved multi-objective dynamic replenishment-pricing model (IDRPM-CPP) is established with the new constraint of the display demand of a single product, and with the non-rigid constraint that the minimum value of the purchasing quantity should not be smaller than the maximum value of the demand, and with the introduction of the service level factor (SLF) updating function.