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...

Full description

Saved in:
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
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0001-1541
1547-5905
DOI:10.1002/aic.16963