Observable augmented systems for sensitivity analysis of Markov and semi-Markov processes

Previous work by the authors (1989) on sensitivity analysis of discrete-event systems (DESs), where an augmented chain approach for estimating performance sensitivities from a single nominal sample path was developed, is extended. In contrast to existing methods, this approach can be applied to cases involving discrete (e.g. integer-valued) parameters such as queue capacities or routing thresholds. However, the basic method is limited by some rather restrictive observability conditions. These conditions are relaxed through an observability transformation which generalizes the method´s applicability. Direct extensions to more general (non-Markovian) DES are also presented via a simplified generalized semi-Markov process reformulation of the approach. Experimental verification of the techniques is included