【摘要】 针对当下蔬菜类商品保质期较短且品质容易下降、急需制定有效的定价和补货策略的问题，提出了一种基于机器学习和遗传算法的蔬菜类商品智能补货决策模型。首先建立基于决策树回归和随机森林的预测模型预测商品的销售总量和成本，然后，建立基于遗传算法的商品收益优化模型并求解出商超未来一周的最大收益，最后给出定价与补货决策。实验结果对多阶段的算法进行了性能分析，验证了此方法的有效性和稳定性，并为供应链管理等领域提供了稳定可靠的优化方案。更多还原

【Abstract】 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 regression 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, providing 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.更多还原