Intelligent replenishment decision model for vegetable products based on machine learning and genetic algorithms
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.
replenishment decisionmachine learningdecision tree regressionrandom forestgenetic algorithm
万方数据
维普
