Pre-processing of Stochastic Petri Nets and an improved Storage Strategy for Proxel Based Simulation

Simulation is a branch of computer science which deals with building the real world entities as models and studying their behavior. Stochastic Petri nets are one such tool for modelling the real world entities. Behavior of models are analyzed by different types of simulation methods. The most common and standard approach is discrete event simulation. But there are still some methods, whose full potential is still to be analyzed. Proxel based simulation is one among them. This thesis has two goals from the proxel based simulation. First goal is to preprocess the stochastic Petri nets. The motivation behind this goal is to automate the processes needed for proxel based simulation. Second goal is to design an improved storage strategy for proxel based simulation. This goal is motivated by the shorter runtime and lower memory requirements for this simulation approach.