Optimal supply chain design and operations under multi‐scale uncertainties: Nested stochastic robust optimization modeling framework and solution algorithm

Yue, Dajun; You, Fengqi

doi:10.1002/aic.15255

Cited by 73 publications

(47 citation statements)

References 81 publications

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“…The inventory level in each manufacturing facility of current time period is determined by transportation flows, production level and inventory level of the previous time period. The inventory degradation after each time period is a fixed percentage of the total inventory level at the previous time period and the inventory degradation rate is considered as given …”

Section: Application To Supply Chain Optimization Under Uncertaintymentioning

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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Section: Application To Supply Chain Optimization Under Uncertaintymentioning

confidence: 99%

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

Zhao

You

2020

AIChE Journal

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“…The stochastic robust optimization framework combines two-stage stochastic programming and ARO [53]. In this subsection, we first analyze the strengths and weaknesses of two-stage stochastic programming and two-stage ARO.…”

Section: Stochastic Robust Optimizationmentioning

confidence: 99%

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

Ning

You

2018

Computers & Chemical Engineering

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119

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A novel data-driven stochastic robust optimization (DDSRO) framework is proposed for optimization under uncertainty leveraging labeled multi-class uncertainty data. Uncertainty data in large datasets are often collected from various conditions, which are encoded by class labels.Machine learning methods including Dirichlet process mixture model and maximum likelihood estimation are employed for uncertainty modeling. A DDSRO framework is further proposed based on the data-driven uncertainty model through a bi-level optimization structure. The outer optimization problem follows a two-stage stochastic programming approach to optimize the expected objective across different data classes; adaptive robust optimization is nested as the inner problem to ensure the robustness of the solution while maintaining computational tractability. A decomposition-based algorithm is further developed to solve the resulting multi-level optimization problem efficiently. Case studies on process network design and planning are presented to demonstrate the applicability of the proposed framework and algorithm.

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“…The final reformulation form of (12) is shown in (19). Using a linear transformation n5C i Á n 0 , we first reformulate the data-driven uncertainty set…”

Section: The Tailored Column-and-constraint Generation Algorithmmentioning

confidence: 99%

“…Among these methods, robust optimization (RO) emerges as a popular approach due to its strong ability to hedge against uncertainties and also because of its computational tractability . RO has a broad array of successful applications in process systems engineering, including process design and synthesis, process scheduling, and supply chain optimization . Traditional RO approaches, also known as static robust optimization (SRO), make all the decisions at once.…”

Section: Introductionmentioning

confidence: 99%

“…[7][8][9][10][11] RO has a broad array of successful applications in process systems engineering, including process design and synthesis, [12][13][14] process scheduling, [15][16][17][18] and supply chain optimization. [19][20][21] Traditional RO approaches, also known as static robust optimization (SRO), 8 make all the decisions at once. Recently, theoretical improvements have been made in bounds of constraint violation.…”

Section: Introductionmentioning

confidence: 99%

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

2017

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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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Optimal supply chain design and operations under multi‐scale uncertainties: Nested stochastic robust optimization modeling framework and solution algorithm

Cited by 73 publications

References 81 publications

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

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

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

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

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