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The location of emergency vehicles is crucial for guaranteeing that populations have access to emergency services and that the provided care is adequate. These location decisions can have an important impact on the mortality and morbidity resulting from emergency episodes occurrence. In this work two robust optimization models are described, that explicitly consider the uncertainty that is inherent in these problems, since it is not possible to know in advance how many will be and where will the emergency occurrences take place. These models consider the minimization of the maximum regret and the maximization of the minimum coverage. They are based on a previous work from the same authors, where they develop a model with innovative features like the possibility of vehicle substitution and the explicit consideration of vehicle unavailability by also representing the dispatching of the vehicles. The developed robust stochastic models have been applied to a dataset composed of Monte Carlo simulation scenarios that were generated from the analysis of real data patterns. Computational results are presented and discussed.