Nonconvex Generalized Benders Decomposition for Stochastic Separable Mixed-Integer Nonlinear Programs
Name
LTB2011_OA.pdf
Description
Accepted version
Size
337.86 KB
Format
Adobe PDF
Checksum (MD5)
3717520d2951db863b1ebfd640e9c9db
Author(s)
Li, Xiang
Tomasgard, Asgeir
Barton, Paul I
Date Issued
July 2011
Journal
Journal of Optimization Theory and Applications
Publisher
Springer Science and Business Media LLC
Citation
Li, Xiang et al. "Nonconvex generalized benders decomposition for stochastic separable mixed-integer nonlinear programs." Journal of Optimization Theory and Applications 151 (December 2011): 425 © 2011 Publisher
Version
Author's final manuscript
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
MIT Department
Massachusetts Institute of Technology. Process Systems Engineering Laboratory
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s10957-011-9888-1
