Sharing cuts under aggregated forecasts when decomposing multi-stage stochastic programs

Abstract Sampling-based decomposition algorithms (SBDAs) solve multi-stage stochastic programs. SBDAs can approximately solve problem instances with many time stages when the stochastic program exhibits interstage dependence in its right-hand side parameters by appropriately sharing cuts. We extend previous methods for sharing cuts in SBDAs, establishing new results under a novel interaction between a class of interstage dependency models, and how they appear in the stochastic program.

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