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    On the relation between flexibility analysis and robust optimization for linear systems

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    Type
    Article
    Authors
    Zhang, Qi
    Grossmann, Ignacio E.
    Lima, Ricardo cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2016-05-31
    Online Publication Date
    2016-05-31
    Print Publication Date
    2016-09
    Permanent link to this record
    http://hdl.handle.net/10754/622123
    
    Metadata
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    Abstract
    Flexibility analysis and robust optimization are two approaches to solving optimization problems under uncertainty that share some fundamental concepts, such as the use of polyhedral uncertainty sets and the worst-case approach to guarantee feasibility. The connection between these two approaches has not been sufficiently acknowledged and examined in the literature. In this context, the contributions of this work are fourfold: (1) a comparison between flexibility analysis and robust optimization from a historical perspective is presented; (2) for linear systems, new formulations for the three classical flexibility analysis problems—flexibility test, flexibility index, and design under uncertainty—based on duality theory and the affinely adjustable robust optimization (AARO) approach are proposed; (3) the AARO approach is shown to be generally more restrictive such that it may lead to overly conservative solutions; (4) numerical examples show the improved computational performance from the proposed formulations compared to the traditional flexibility analysis models. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3109–3123, 2016
    Citation
    Zhang Q, Grossmann IE, Lima RM (2016) On the relation between flexibility analysis and robust optimization for linear systems. AIChE Journal 62: 3109–3123. Available: http://dx.doi.org/10.1002/aic.15221.
    Sponsors
    The authors gratefully acknowledge the financial support from the National Science Foundation under Grant No. 1159443.
    Publisher
    Wiley
    Journal
    AIChE Journal
    DOI
    10.1002/aic.15221
    Additional Links
    http://onlinelibrary.wiley.com/doi/10.1002/aic.15221/full
    ae974a485f413a2113503eed53cd6c53
    10.1002/aic.15221
    Scopus Count
    Collections
    Articles; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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