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

作者：Li Xiang; Tomasgard Asgeir; Barton Paul I^*

来源：Journal of Optimization Theory and Applications, 2011, 151(3): 425-454.

DOI：10.1007/s10957-011-9888-1

• Stochastic programming
• Mixed-integer nonlinear programming
• Decomposition algorithm
• Global optimization

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.

• 出版日期2011-12
• 单位MIT