基于历史数据的蔬菜类商品定价与补货决策模型

Pricing and Replenishment Decision Model for Vegetable Commodities Based on Historical Data

深入探讨了蔬菜类商品的定价与补货策略,首先,对蔬菜流水数据处理缺失值和异常值,从中汇总各单品或品类的销量数据;随后,引入Prophet模型处理销量,拆分出销售额数据的趋势、季节和节假日成分,并将销售价格作为外生变量,确定品类销售额与成本加成比率之间的关联;最后,将模拟退火算法、遗传算法同Prophet模型相结合,迭代求解最优定价和补货策略.此外,本文还将库存空间、储藏时间等因素纳入考虑,并提出了动态规划模型作为补货和定价模型的补充.

We delve into the pricing and replenishment strategies for vegetable commodities.Initially,we address missing and outlier values in the vegetable flow data,summarizing sales data for each item or category.We introduce the Prophet model to process sales volume,decomposing the sales data into trend,seasonal,and holiday components,and incorporating selling price as an exogenous variable.This helps in determining the association between category sales and cost markup ratios.Subsequently,we combine simulated annealing and genetic algorithms with the Prophet model to iteratively solve for the optimal pricing and replenishment strategies.Moreover,we incorporate factors like inventory space and storage time into the model and propose a dynamic programming model as a supplement to the replenishment and pricing model.