Traditional Stochastic High-Level Petri Nets (SHLPNs) can efficiently simplify system models by folding more than one homogeneous subsystem into one subsystem

and obviously reduce state space size by grouping more than one marking with the same token distribution into one marking.The grouped marking is called as Compound Markings (CMs).However

this method can only be applied to the systems consisting of homogeneous subsystems.This paper extends the traditional method to solve this problem that traditional SHLPNs are only limited to homogeneous systems

by proposing asymmetrical transition-firing predication and extended compound marking concept.The new method can model and analyze inhomogeneous systems exactly.Furthermore

it retains the SHLPNs advantages of simplifying models and reducing state space size.