|Title||Nonconvex Generalized Benders Decomposition for Stochastic Separable Mixed-Integer Nonlinear Programs|
|Publication Type||Journal Article|
|Year of Publication||2011|
|Authors||Li, X., A. Tomasgard, and P. I. Barton|
|Journal||Journal of Optimization Theory and Applications|
|Keywords||Decomposition algorithm, global optimization, Mixed-integer nonlinear programming, stochastic programming|
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.