In response to the current challenges of short shelf life and quality deterioration in vegetable products,as well as the urgent need for effective pricing and replenishment strategies,this paper proposes an intelligent replenishment decision model for vegetable products based on machine learning and genetic algorithms.Firstly,a predictive model based on decision tree regres-sion and random forest is established to predict the total sales and costs of the products.Finally,an optimization model for product revenue based on genetic algorithms is developed to determine the maximum profit for the upcoming week in supermarkets,provid-ing pricing and replenishment decisions.Experimental results include performance analyses of multi-stage algorithms,validating the effectiveness and stability of the proposed method.This research offers a stable and reliable optimization solution for areas such as supply chain management.
关键词
补货决策/机器学习/决策树回归/随机森林/遗传算法
Key words
replenishment decision/machine learning/decision tree regression/random forest/genetic algorithm
维普
万方数据