论文标题
制造交货时间的概率模型
Probabilistic Models for Manufacturing Lead Times
论文作者
论文摘要
在这项研究中，我们利用高斯过程，概率神经网络，自然梯度增强和分位数回归增强的梯度增强，以模拟激光制造过程的交付时间。我们在域中介绍了概率建模，并根据不同能力比较模型。在现实生活数据中的模型之间进行比较时，我们的工作具有许多用例和实质性业务价值。我们的结果表明，所有模型都超过了使用域经验的公司估计基准，并具有良好的经验频率校准。
In this study, we utilize Gaussian processes, probabilistic neural network, natural gradient boosting, and quantile regression augmented gradient boosting to model lead times of laser manufacturing processes. We introduce probabilistic modelling in the domain and compare the models in terms of different abilities. While providing a comparison between the models in real-life data, our work has many use cases and substantial business value. Our results indicate that all of the models beat the company estimation benchmark that uses domain experience and have good calibration with the empirical frequencies.
