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• W3080639648 abstract "Endogenous, i.e. decision-dependent, uncertainty has received increased interest in the stochastic programming community. In the robust optimization context, however, it has rarely been considered. This work addresses multistage robust mixed-integer optimization with decision-dependent uncertainty sets. The proposed framework allows us to consider both continuous and integer recourse, including recourse decisions that affect the uncertainty set. We derive a tractable reformulation of the problem by leveraging recent advances in the construction of nonlinear decision rules, and introduce discontinuous piecewise linear decision rules for continuous recourse. Computational experiments are performed to gain insights on the impact of endogenous uncertainty, the benefit of discrete recourse, and computational performance. Our results indicate that the level of conservatism in the solution can be significantly reduced if endogenous uncertainty and mixed-integer recourse are properly modeled." @default.
• W3080639648 created "2020-09-01" @default.
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• W3080639648 date "2020-08-26" @default.
• W3080639648 modified "2023-09-25" @default.
• W3080639648 title "Multistage Robust Mixed-Integer Optimization Under Endogenous Uncertainty" @default.
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• W3080639648 doi "https://doi.org/10.48550/arxiv.2008.11633" @default.
• W3080639648 hasPublicationYear "2020" @default.
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