Randomization is an attractive alternative for the transient analysis of continuous time Markov
models. The main advantages of the method are numerical stability, well-controlled computation
error and ability to specify the computation error in advance. However, the fact that the
method can be computationally expensive limits its applicability. Recently, a variant of the
(standard) randomization method, called split regenerative randomization has been proposed
for the efficient analysis of reliability-like models of fault-tolerant systems with deferred repair. In this paper, we generalize that method so that it covers more general reward measures: the
expected transient reward rate and the expected averaged reward rate. The generalized method
has the same good properties as the standard randomization method and, for large models and
large values of the time t at which the measure has to be computed, can be significantly less
expensive. The method requires the selection of a subset of states and a regenerative state satisfying some conditions. For a class of continuous time Markov models, class C'_2, including
typical failure/repair reliability models with exponential failure and repair time distributions
and deferred repair, natural selections for the subset of states and the regenerative state exist and results are available assessing approximately the computational cost of the method in terms of
“visible” model characteristics. Using a large model class C'_2 example, we illustrate the performance of the method and show that it can be significantly faster than previously proposed
randomization-based methods.
