期刊
JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS
卷 151, 期 3, 页码 425-454出版社
SPRINGER/PLENUM PUBLISHERS
DOI: 10.1007/s10957-011-9888-1
关键词
Stochastic programming; Mixed-integer nonlinear programming; Decomposition algorithm; Global optimization
资金
- Statoil
- research council of Norway [176089/S60]
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 epsilon-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.
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