GSPM models: sensitivity analysis and applications

Sensitivity analysis of continuous time Markov chains has been considered recently by several re­ searchers. This is very useful in performing bottle­ neck analysis and optimization on systems especially during the design stage. However the construction of these large and complex Markov models is tedious and error-prone process. Generalized Stochastic Petri Nets (GSPN) provide a very useful high-level inter­ face for the automatic generation of the underlying Markov chain. This paper extends parametric sensi­ tivity analysis to GSPN models. The rates and proba­ bilities of the transitions of GSPN models are defined as functions of an independent variable. Equations for the sensitivity analysis of steady-state and transient measures of GSPN and GSPN reward models are de­ veloped and implemented in a software package. An example illustrating the use of sensitivity analysis is presented.