Short Paper - A Note on Robust Combinatorial Optimization with Generalized Interval Uncertainty

A short note on the robust combinatorial optimization problems with cardinality constrained uncertainty

Robust combinatorial optimization problems with cardinality constrained uncertainty may be solved by a finite number of nominal problems. In this paper, we show that the number of nominal problems to be solved can be reduced significantly.

Robust combinatorial optimization with cost uncertainty

We present in this paper a new model for robust combinatorial optimization with cost uncertainty that generalizes the classical budgeted uncertainty set. We suppose here that the budget of uncertainty is given by a function of the problem variables, yielding an uncertainty multifunction. The new model is less conservative than the classical model and approximates better Valueat-Risk objective f...

Robust combinatorial optimization with variable budgeted uncertainty

Abstract: We introduce a new model for robust combinatorial optimization where the uncertain parameters belong to the image of multifunctions of the problem variables. In particular, we study the variable budgeted uncertainty, an extension of the budgeted uncertainty introduced by Bertsimas and Sim. Variable budgeted uncertainty can provide the same probabilistic guarantee as the budgeted uncer...

Robust Combinatorial Optimization under Budgeted-Ellipsoidal Uncertainty∗

In the field of robust optimization uncertain data is modeled by uncertainty sets, i.e. sets which contain all relevant outcomes of the uncertain parameters. The complexity of the related robust problem depends strongly on the shape of the uncertainty set. Two popular classes of uncertainty are budgeted uncertainty and ellipsoidal uncertainty. In this paper we introduce a new uncertainty class ...

Contributions to robust combinatorial optimization with budgeted uncertainty