We propose a model for recoverable robust optimization with commitment. Given a combinatorial optimization problem and uncertainty about elements that may fail, we ask for a robust solution that, after the failing elements are revealed, can be augmented in a limited way. More specifically, in our model we focus on preserving the non-failing elements of the initial solution by committing to them. We settle the computational complexity of such a robust counterpart of various classical polynomial-time solvable combinatorial optimization problems.
[Komal Muluk] (Aachen University)
[ https://www.unravel.rwth-aachen.de/cms/unravel/das-graduiertenkolleg/te… | https://www.unravel.rwth-aachen.de/cms/unravel/das-graduiertenkolleg/te… ]
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