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A scenario-based approach for robust linear optimization

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Finding robust solutions of an optimization problem is an important issue in practice. The established concept of Ben-Tal et al. [2] requires that a robust solution is feasible for all possible scenarios. However, this concept is very conservative and hence may lead to solutions with a bad objective value and is in many cases hard to solve. Thus it is not suitable for most practical applications. In this paper we suggest an algorithm for calculating robust solutions that is easy to implement and not as conservative as the strict robustness approach. We show some theoretical properties of our approach and evaluate it using linear programming problems from NetLib.