Performance analysis of chi models using discrete-time probabilistic reward graphs

We propose the model of discrete-time probabilistic reward graphs (DTPRGs) for performance analysis of systems exhibiting discrete deterministic time delays and probabilistic behavior, via their interpretation as discrete-time Markov reward chains, full-fledged platform for qualitative and quantitative analysis of timed systems based on the modeling language chi. The extension proposed in this paper is based on timed branching bisimulation reduction followed by a tailored inclusion of probabilities and rewards. The approach is applied in an industrial case study of a turntable drill. The resulting performance measures are shown to be comparable to those obtained by existent methods of the chi environment, viz. simulation and continuous-time Markovian analysis