Data‐driven adaptive nested robust optimization: General modeling framework and efficient computational algorithm for decision making under uncertainty

A novel data‐driven adaptive robust optimization framework that leverages big data in process industries is proposed. A Bayesian nonparametric model—the Dirichlet process mixture model—is adopted and combined with a variational inference algorithm to extract the information embedded within uncertain...

Full description

Saved in:
Bibliographic Details
Published inAIChE journal Vol. 63; no. 9; pp. 3790 - 3817
Main Authors Ning, Chao, You, Fengqi
Format Journal Article
LanguageEnglish
Published New York American Institute of Chemical Engineers 01.09.2017
Subjects
Adaptive algorithms
Algorithms
Bayesian analysis
Columns (process)
Computational efficiency
Computer applications
Data management
Decision making
Dirichlet problem
Industrial applications
Information processing
Learning algorithms
Machine learning
Multilevel
Nonlinear programming
Optimization
Robustness (mathematics)
Scheduling
Solvers
Uncertainty
Online AccessGet full text
ISSN0001-1541
1547-5905
DOI10.1002/aic.15717

Cover

More Information
Summary:A novel data‐driven adaptive robust optimization framework that leverages big data in process industries is proposed. A Bayesian nonparametric model—the Dirichlet process mixture model—is adopted and combined with a variational inference algorithm to extract the information embedded within uncertainty data. Further a data‐driven approach for defining uncertainty set is proposed. This machine‐learning model is seamlessly integrated with adaptive robust optimization approach through a novel four‐level optimization framework. This framework explicitly accounts for the correlation, asymmetry and multimode of uncertainty data, so it generates less conservative solutions. Additionally, the proposed framework is robust not only to parameter variations, but also to anomalous measurements. Because the resulting multilevel optimization problem cannot be solved directly by any off‐the‐shelf solvers, an efficient column‐and‐constraint generation algorithm is proposed to address the computational challenge. Two industrial applications on batch process scheduling and on process network planning are presented to demonstrate the advantages of the proposed modeling framework and effectiveness of the solution algorithm. © 2017 American Institute of Chemical Engineers AIChE J , 63: 3790–3817, 2017
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0001-1541
1547-5905
DOI:10.1002/aic.15717