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...
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| Published in | AIChE journal Vol. 63; no. 9; pp. 3790 - 3817 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
New York
American Institute of Chemical Engineers
01.09.2017
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0001-1541 1547-5905 |
| DOI | 10.1002/aic.15717 |
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| Abstract | 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 |
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| AbstractList | 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 |
| Author | You, Fengqi Ning, Chao |
| Author_xml | – sequence: 1 givenname: Chao surname: Ning fullname: Ning, Chao organization: Smith School of Chemical and Biomolecular Engineering Cornell University Ithaca New York 14853 – sequence: 2 givenname: Fengqi orcidid: 0000-0001-9609-4299 surname: You fullname: You, Fengqi organization: Smith School of Chemical and Biomolecular Engineering Cornell University Ithaca New York 14853 |
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| SubjectTerms | 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 |
| Title | Data‐driven adaptive nested robust optimization: General modeling framework and efficient computational algorithm for decision making under uncertainty |
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