Abstract
In this paper, we propose a decomposition-based branch-and-bound (DBAB) algorithm for solving two-stage stochastic programs having mixed-integer first- and second-stage variables. A modified Benders' decomposition method is developed, where the Benders' subproblems define lower bounding second-stage value functions of the first-stage variables that are derived by constructing a certain partial convex hull representation of the two-stage solution space. This partial convex hull is sequentially generated using a convexification scheme such as the Reformulation-Linearization Technique (RLT) or lift-and-project process, which yields valid inequalities that are reusable in the subsequent subproblems by updating the values of the first-stage variables. A branch-and-bound algorithm is designed based on a hyperrectangular partitioning process, using the established property that any resulting lower bounding Benders' master problem defined over a hyperrectangle yields the same objective value as the original stochastic program over that region if the first-stage variable solution is an extreme point of the defining hyperrectangle or the second-stage solution satisfies the binary restrictions. We prove that this algorithm converges to a global optimal solution. Some numerical examples and computational results are presented to demonstrate the efficacy of this approach.
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Sherali, H., Zhu, X. On solving discrete two-stage stochastic programs having mixed-integer first- and second-stage variables. Math. Program. 108, 597–616 (2006). https://doi.org/10.1007/s10107-006-0724-6
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DOI: https://doi.org/10.1007/s10107-006-0724-6
Keywords
- Two-stage stochastic mixed-integer programs
- Benders' decomposition
- Convexification
- Reformulation-Linearization Technique (RLT)