Distributionally Robust Chance Constrained Programming with Generative Adversarial Networks (GANs)
This is an incremental improvement for supply chain optimization, combining existing GAN and robust optimization methods in a novel way.
The paper tackles supply chain optimization under demand uncertainty by developing a GAN-based distributionally robust chance constrained programming framework, achieving efficient uncertainty modeling through an end-to-end differentiable approach.
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.