Nonconvex Generalized Benders Decomposition for Stochastic Separable Mixed-Integer Nonlinear Programs

Xiang Li, Asgeir Tomasgard and Paul I. Barton ()
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Xiang Li: Massachusetts Institute of Technology
Asgeir Tomasgard: Norwegian University of Science and Technology
Paul I. Barton: Massachusetts Institute of Technology

Journal of Optimization Theory and Applications, 2011, vol. 151, issue 3, No 1, 425-454

Abstract: Abstract This paper considers deterministic global optimization of scenario-based, two-stage stochastic mixed-integer nonlinear programs (MINLPs) in which the participating functions are nonconvex and separable in integer and continuous variables. A novel decomposition method based on generalized Benders decomposition, named nonconvex generalized Benders decomposition (NGBD), is developed to obtain ε-optimal solutions of the stochastic MINLPs of interest in finite time. The dramatic computational advantage of NGBD over state-of-the-art global optimizers is demonstrated through the computational study of several engineering problems, where a problem with almost 150,000 variables is solved by NGBD within 80 minutes of solver time.

Keywords: Stochastic programming; Mixed-integer nonlinear programming; Decomposition algorithm; Global optimization (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (27)

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DOI: 10.1007/s10957-011-9888-1

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