Discrete Event Formalisms for Workflow
Associate Professor Calin I. Ciufudean
"Stefan Cel Mare" Universtity of Suceava
Faculty of Electrical Engineering and Computer Science
Department of Automatics and Computers
9, University str., RO720225, Suceava
We focus on estimating the throughput of a workflow
modelled with stochastic Petri nets (SPNs). We consider
this discussion important, as there is a lot of
confusion about the definition of the risk and the
reliability of flexible manufacturing system analysis,
both being risk analysts and decision makers. We propose
an approach for this analysis by using a new model for
artificial social systems (ASSs) behaviours, and by
introducing equivalent transfer functions for SPNs.
ASSs exist in practically every multi-agent system, and
play a major role in the performance and effectiveness
chart of the agents. ASS allows agents to coexist in a
shared environment and pursue their respective goals in
the presence of other agents.
This is the reason why we introduce a suggestive model
for ASSs. To model complex systems, such as flexible
manufacturing ones, a class of Petri nets is adopted,
and briefly introduced. This class allows representing
the flow of physical resources and control information
data of the ASSs components. In the analysis of SPN we
use simulations in respect to timing parameters in a
generalized semi-Markov process (GSMP). By using
existing results on perturbation analysis (e.g., delays
in supply with raw materials, equipment failure, etc.),
and by extending them to new physical interpretations we
address unbiased sensitivity estimators correlated with
practical solutions in order to attenuate the
The novelty of the approach is that the construction of
large Markov chains is not required. Using a structural
decomposition, the construction system is divided into
cells. We can simplify the structure of the SPN using
the presented approach, which is useful when we deal
with complex Petri nets, and we need to simplify these
structures (e.g. graphs) in order to analyze them
properly. For each cell a Markov model was derived and
the probability was determined of at least Ni working
machines in cell i, for i = 1, 2, .., n and j, where j =
1, ..., m, working material handling system (MHS) at
time t, where Ni and j satisfy the system production
capacity requirements. An example illustrates this
approach. The results reported here form the basis of
several enhancements, such as conducting performance
studies of complex systems with multiple part types.
Brief Biography of the Speaker:
• Honorary Member of the Romanian Society of Electrical
& Control Engineering - Member of the Romanian Technical
• Technical Expert of the Romanian Ministry of Justice.
• President of the Romanian Society of Electrical &
Control Engineering, Suceava Branch.
• Academic Positions: Assoc. Professor, Dept. of
Automatics and Computers, Faculty of Electrical
Engineering and Computer Science, “Stefan cel Mare”
University of Suceava, Romania.
• Fields of Scientific Activities: Discrete Event
Systems, Complex Measurement Systems, Reliability and
Diagnosis of Control Systems, Environmental Management.
• He published 6 books and over 120 scientific papers in
conference proceedings and journals.