Distributionally robust chance constrained programming with generative adversarial networks (GANs)

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 dat...

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Bibliographic Details
Published in:	AIChE journal Vol. 66; no. 6
Main Authors:	Zhao, Shipu, You, Fengqi
Format:	Journal Article
Language:	English
Published:	Hoboken, USA John Wiley & Sons, Inc 01.06.2020 American Institute of Chemical Engineers
Subjects:	Approximation Approximation method Artificial neural networks Biofuels Computer applications data‐driven method deep learning distributionally robust chance‐constrained programming generative adversarial network Generative adversarial networks Information processing Machine learning Neural networks Optimization Parameter uncertainty Programming Robustness supply chain optimization Supply chains
ISSN:	0001-1541, 1547-5905
Online Access:	Get full text
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