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Center for Nonlinear Studies |
Two-stage stochastic programming is one of the approaches to model decision-making under uncertainty. Although there have been algorithmic advances in two-stage stochastic mixed-integer linear programs in the past two decades, the algorithms to address the nonlinear counterpart are few. In this talk, I will first give an overview of the recent advances in solving stochastic MINLP problems. The major focus of this talk is a generalized Benders decomposition-based branch and cut algorithm that combines strengthened Benders cuts and Lagrangean cuts for solving two stage stochastic MINLPs with mixed-binary first and second stage variables. The proposed algorithm is applied to a stochastic pooling problem, a crude selection problem, and a storage design problem. The performance of the proposed algorithm is compared with a Lagrangean decomposition-based branch and bound algorithm and solving the corresponding deterministic equivalent with the solvers including BARON, ANTIGONE, and SCIP.
Host: Harsha Nagarajan