Abstract
An interesting modeling problem is the need to model one or more of the system modules without exposition to the other system modules. This modeling problem arises due to our interest in these modules or incomplete knowledge, or inherent complexity, of the rest of the system modules. Whenever the performance measures (one or more) of the desired modules are available through previous performance studies, data sheets, or previous experimental works, the required performance measures for the whole system can be predicted from our proposed modeling technique. The incomplete knowledge problem of the dynamic behavior of some system modules has been studied by control theory. In the control area, such systems are known as partially observed discrete event dynamic systems, or POS systems. To the best of our knowledge, the performance evaluation of the POS system has not been addressed by the Petri net theory yet. Therefore, in this paper, we propose a new modeling technique for solving this kind of problem based on using the Petri net theory (i.e. Stochastic Reward Nets (SRNs)) in conjunction with the optimal control theory.