Introduction
Robust optimization is a modelling paradigm that offers solutions when uncertainty in the input data can be bounded within a well described region.
Robust optimization is different from stochastic optimization. While stochastic optimization usually aims to identify a solution whose expected value of the objective function is optimal with respect to the probability distribution of the uncertain data, robust optimization focuses on finding a solution that is feasible regardless of the realization of the uncertain values; hence the term robust. When uncertainty is associated with the objective of a model, robust optimization returns a solution that is optimal with respect to the worst case of all realizations of the uncertain quantities.
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