Data-driven stochastic robust optimization: General computational framework and algorithm leveraging machine learning for optimization under uncertainty in the big data era

Ning, Chao; You, Fengqi

doi:10.1016/j.compchemeng.2017.12.015

Cited by 119 publications

(38 citation statements)

References 72 publications

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“…In some applications, uncertainty data in large datasets are usually collected under multiple conditions. A data-driven stochastic robust optimization framework was proposed for optimization under uncertainty leveraging labeled multi-class uncertainty data [155]. Machine learning methods…”

Section: Data-driven Robust Optimizationmentioning

confidence: 99%

Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming

Ning

You

2019

Computers & Chemical Engineering

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261

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This paper reviews recent advances in the field of optimization under uncertainty via a modern data lens, highlights key research challenges and promise of data-driven optimization that organically integrates machine learning and mathematical programming for decision-making under uncertainty, and identifies potential research opportunities. A brief review of classical mathematical programming techniques for hedging against uncertainty is first presented, along with their wide spectrum of applications in Process Systems Engineering. A comprehensive review and classification of the relevant publications on data-driven distributionally robust optimization, data-driven chance constrained program, data-driven robust optimization, and data-driven scenario-based optimization is then presented. This paper also identifies fertile avenues for future research that focuses on a closed-loop data-driven optimization framework, which allows the feedback from mathematical programming to machine learning, as well as scenario-based optimization leveraging the power of deep learning techniques. Perspectives on online learningbased data-driven multistage optimization with a learning-while-optimizing scheme is presented.

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Section: Data-driven Robust Optimizationmentioning

confidence: 99%

Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming

Ning

You

2019

Computers & Chemical Engineering

Self Cite

261

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“…A number of machine learning techniques such as RKDE, 51,53 Bayesian model, 49 and principal component analysis, 52 have been proposed to construct uncertainty sets from data.…”

Section: Affine Decision Rule For Two-stage Aromentioning

confidence: 99%

“…46,47 A number of frameworks of data-driven ARO methods were proposed by organically integrating machine learning algorithms with robust optimization methods. [48][49][50][51][52] Despite the growing popularity of developing robust optimization methods for process optimization, there is no relevant SRO or ARO framework for operational optimization of steam systems under uncertainty, to the best of our knowledge.…”

Section: Introductionmentioning

confidence: 99%

Operational optimization of industrial steam systems under uncertainty using data‐Driven adaptive robust optimization

2018

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This paper addresses the operational optimization of industrial steam systems under device efficiency uncertainty using a data-driven adaptive robust optimization approach. A semi-empirical model of steam turbine is first developed based on process mechanism and operational data. Uncertain parameters of the proposed steam turbine model are further derived from the historical process data. A robust kernel density estimation method is then employed to construct the uncertainty sets for modeling these uncertain parameters. The data-driven uncertainty sets are incorporated into a two-stage adaptive robust mixed-integer linear programming (MILP) framework for operational optimization of steam systems to minimize the total operating cost. Integer variables are introduced to model the on/off decisions of the steam turbines and electrical motors, which are the major energy consumers of the steam system. By applying the affine decision rule, the proposed multi-level optimization model is transformed into its robust counterpart, which is a single-level MILP problem. The proposed framework is applied to the steam system of a real-world ethylene plant to demonstrate its applicability.

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“…Compared with other machine learning methods, complex correlations and even some hidden modes within data can be found by deep learning methods . In data‐driven optimization frameworks, uncertainty is usually modeled based on available data . Therefore, by efficiently extracting high‐level features of data, deep learning has become a powerful method to model uncertainty in the data‐driven optimization frameworks…”

Section: Introductionmentioning

confidence: 99%

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

Zhao

You

2020

AIChE Journal

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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.

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Data-driven stochastic robust optimization: General computational framework and algorithm leveraging machine learning for optimization under uncertainty in the big data era

Cited by 119 publications

References 72 publications

Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming

Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming

Operational optimization of industrial steam systems under uncertainty using data‐Driven adaptive robust optimization

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

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