  In the context of Markov decision processes running in continuous time, one
of the most intriguing challenges is the efficient approximation of finite
horizon reachability objectives. A multitude of sophisticated model checking
algorithms have been proposed for this. However, no proper benchmarking has
been performed thus far.
  This paper presents a novel and yet simple solution: an algorithm originally
developed for a restricted subclass of models and a subclass of schedulers can
be twisted so as to become competitive with the more sophisticated algorithms
in full generality. As the second main contribution, we perform a comparative
evaluation of the core algorithmic concepts on an extensive set of benchmarks
varying over all key parameters: model size, amount of non-determinism, time
horizon, and precision.
