论文标题
通过生成对抗网络（GAN），分布稳健的机会限制了编程
Distributionally Robust Chance Constrained Programming with Generative Adversarial Networks (GANs)
论文作者
论文摘要
本文介绍了一种基于深度学习的新型基于数据驱动的优化方法。提出了一个基于数据驱动的新型生成对抗网络（GAN）的分布稳健的频率约束编程框架。 GAN用于以非参数和无监督的方式从历史数据中完全从历史数据中提取分布信息，而没有先验的近似或假设。由于GAN利用了深层神经网络，因此可以学习复杂的数据分布和模式，并且可以有效，准确地建模不确定性。分布稳健的机会约束编程会考虑到不确定参数的模棱两可的概率分布。为了应对计算挑战，采用了样本平均近似方法，并且GAN以端到端的方式通过可区分的网络生成所需的数据样本。然后将提出的框架应用于需求不确定性下的供应链优化。通过县级案例研究，伊利诺伊州的空间生物燃料供应链的县级案例研究说明了拟议方法的适用性。
This paper presents a novel deep learning based data-driven optimization method. A novel generative adversarial network (GAN) based data-driven distributionally robust chance constrained programming framework is proposed. GAN is applied to fully extract distributional information from historical data in a nonparametric and unsupervised way without a priori approximation or assumption. Since GAN utilizes deep neural networks, complicated data distributions and modes can be learned, and it can model uncertainty efficiently and accurately. Distributionally robust chance constrained programming takes into consideration ambiguous probability distributions of uncertain parameters. To tackle the computational challenges, sample average approximation method is adopted, and the required data samples are generated by GAN in an end-to-end way through the differentiable networks. The proposed framework is then applied to supply chain optimization under demand uncertainty. The applicability of the proposed approach is illustrated through a county-level case study of a spatially explicit biofuel supply chain in Illinois.
