ABSTRACT
Events are any potential change in the state of a dynamic system. Event relationship graphs (ERGs) explicitly model the ways in which one system event may cause another system event to occur. The cause and effect relationships between events modeled in an ERG, along with simple rules for execution and initial conditions, completely specify all possible sample paths (state trajectories) of a dynamic system model. Continuous dynamics systems have been modeled as ERGs, but they are most commonly used to model discrete-event system dynamics. The ERG for a queueing system is typically a system of simple difference equations analogous to a system of differential equations used in modeling continuous time system dynamics. ERGs are completely general in that any dynamic system can be modeled as an ERG (Savage et al, 2005). They are easy to develop and understand and facilitate the design of efficient simulation models. ERGs also have analytical representations that aid in systems analysis, specifically when the potential system trajectories for ERG model are represented as the solutions to mathematical optimization problems.
