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    A joint decomposition method for global optimization of multiscenario nonconvex mixed-integer nonlinear programs

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    Ogbe&Li2019_share.pdf (434.9Kb)
    Date
    2019-11-01
    Author
    Ogbe, Emmanuel
    Li, Xiang
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    Abstract
    This paper proposes a joint decomposition method that combines Lagrangian decomposition and generalized Benders decomposition, to efficiently solve multiscenario nonconvex mixed-integer nonlinear programming (MINLP) problems to global optimality, without the need for explicit branch and bound search. In this approach, we view the variables coupling the scenario dependent variables and those causing nonconvexity as complicating variables. We systematically solve the Lagrangian decomposition subproblems and the generalized Benders decomposition subproblems in a unified framework. The method requires the solution of a difficult relaxed master problem, but the problem is only solved when necessary. Enhancements to the method are made to reduce the number of the relaxed master problems to be solved and ease the solution of each relaxed master problem. We consider two scenario-based, two-stage stochastic nonconvex MINLP problems that arise from integrated design and operation of process networks in the case study, and we show that the proposed method can solve the two problems significantly faster than state-of-the-art global optimization solvers.
    URI for this record
    http://hdl.handle.net/1974/27985
    External DOI
    https://dx.doi.org/10.1007/s10898-019-00786-x
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    • Department of Chemical Engineering Faculty Publications
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