  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.
