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dc.contributor.authorOgbe, Emmanuel
dc.contributor.authorLi, Xiang
dc.date.accessioned2020-07-29T18:46:51Z
dc.date.available2020-07-29T18:46:51Z
dc.date.issued2019-11-01
dc.identifier.citationOgbe, E., & Li, X. (2019). A joint decomposition method for global optimization of multiscenario nonconvex mixed-integer nonlinear programs. Journal of Global Optimization, 75(3), 595–629. doi:10.1007/s10898-019-00786-xen
dc.identifier.urihttp://hdl.handle.net/1974/27985
dc.description.abstractThis 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.en
dc.publisherSpringer Science and Business Media LLCen
dc.rightsFinal publication available at: http://dx.doi.org/10.1007/s10898-019-00786-xen
dc.titleA joint decomposition method for global optimization of multiscenario nonconvex mixed-integer nonlinear programsen
dc.typejournal articleen
dc.identifier.doihttps://dx.doi.org/10.1007/s10898-019-00786-x
project.funder.identifier10.13039/501100000038
project.funder.nameNatural Sciences and Engineering Research Council of Canadaen
oaire.awardNumberCRDPJ 485798-15
oaire.awardNumberRGPIN 418411-13


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