Abstract

Solving optimization problems is normally done with deterministic data. These data are however not always known with certainty. Recoverable robustness handles this uncertainty by modeling it in different scenarios. An initial solution is found and can be made feasible in each scenario by applying a given and simple recovery algorithm
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