Algorithms for the generation of state-level representations of stochastic activity networks with general reward structures

Stochastic Petri nets (SPNs) and extensions are a popular method for evaluating a wide variety of systems. In most cases, their numerical solution requires generating a state-level stochastic process, which captures the behavior of the SPN with respect to a set of specified performance measures. These measures are commonly defined at the net level by means of a reward variable. In this paper, we discuss issues regarding the generation of state-level reward models for systems specified as stochastic activity networks (SANs) with “step-based reward structures”. Step-based reward structures are a generalization of previously proposed reward structures for SPNs and can represent all reward variables that can be defined on the marking behavior of a net. While discussing issues related to the generation of the underlying state-level reward model, we provide an algorithm to determine whether a given SAN is “well-specified” A SAN is well-specified if choices about which instantaneous activity completes among multiple simultaneously-enabled instantaneous activities do not matter, with respect to the probability of reaching next possible stable markings and the distribution of reward obtained upon completion of a timed activity. The fact that a SAN is well specified is both a necessary and sufficient condition for its behavior to be completely probabilistically specified, and hence is an important property to determine