Applying deep learning to the newsvendor problem
Applying deep learning to the newsvendor problem
复制标题
DOI：
10.1080/24725854.2019.1632502
复制
发表时间：
2016-07
影响因子：
2.6
通讯作者：
Afshin Oroojlooyjadid;L. Snyder;Martin Takác
中科院分区：
文献类型：
--
作者：
Afshin Oroojlooyjadid;L. Snyder;Martin Takác
摘要报童问题是最基本、应用最广泛的库存模型之一。如果需求的概率分布是已知的，问题可以解析地解决。然而，近似的概率分布是不容易的，容易出错，因此，得到的解决方案报童问题可能不是最佳的。为了解决这个问题，我们提出了一种基于深度学习的算法，该算法根据需求数据的特征优化所有产品的订单数量。我们的算法集成了预测和库存优化步骤，而不是像通常那样单独求解它们，并且不需要了解需求的概率分布。人们可以将最优订单数量视为深度神经网络中的标签。然而，与大多数深度学习应用程序不同的是，我们的模型并不知道真正的标签（订单数量），而是在训练过程中学习它们。对真实数据的数值实验表明，我们的算法优于其他方法，包括数据驱动和机器学习方法，特别是对于高波动性的需求。最后，为了说明这种方法可以用于其他库存优化问题，我们提供了一个扩展（r，Q）的政策。
Abstract The newsvendor problem is one of the most basic and widely applied inventory models. If the probability distribution of the demand is known, the problem can be solved analytically. However, approximating the probability distribution is not easy and is prone to error; therefore, the resulting solution to the newsvendor problem may not be optimal. To address this issue, we propose an algorithm based on deep learning that optimizes the order quantities for all products based on features of the demand data. Our algorithm integrates the forecasting and inventory-optimization steps, rather than solving them separately, as is typically done, and does not require knowledge of the probability distributions of the demand. One can view the optimal order quantities as the labels in the deep neural network. However, unlike most deep learning applications, our model does not know the true labels (order quantities), but rather learns them during the training. Numerical experiments on real-world data suggest that our algorithm outperforms other approaches, including data-driven and machine learning approaches, especially for demands with high volatility. Finally, in order to show how this approach can be used for other inventory optimization problems, we provide an extension for (r, Q) policies.