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Algebraically structured colored Petri nets to model sequential processes.

M Bourcerie¹, J Y Morel

¹Dept. of Electr. Comput. & Syst. Eng., Angers Univ.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|January 1, 1997

Summary

This summary is machine-generated.

Colored Petri Nets (CPN) struggle with complex sequential processes. This study introduces a novel hybrid CPN-FIFO queue model with symbolic polynomial calculations for enhanced sequence modeling, including failure recovery.

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Area of Science:

Computer Science
Discrete Mathematics

Background:

Standard Colored Petri Nets (CPN) lack efficient mechanisms for modeling complex sequential processes.
Existing methods using CPN functions like "Succ" and "Prec" are limited to simple cases.
Defining complex sequences requires cumbersome, static tables, hindering dynamic modeling.

Purpose of the Study:

To develop an improved modeling approach for complex sequential processes.
To overcome the limitations of standard Colored Petri Nets in representing intricate sequences.
To introduce a method enabling symbolic calculations for enhanced CPN modeling.

Main Methods:

Introduction of a hybrid structure combining Colored Petri Nets (CPN) with First-In, First-Out (FIFO) queues.
Definition of an isomorphism between the set of colors and a finite field Z/pZ.
Utilization of polynomial calculations associated with arcs for symbolic computation, replacing traditional linear functions.

Main Results:

The proposed hybrid model effectively addresses the limitations of standard CPN for sequential processes.
Symbolic calculation using polynomials enables more flexible and powerful modeling.
Demonstrated capability in modeling complex sequences, including scenarios with failure recovery.

Conclusions:

The novel CPN-FIFO hybrid structure with polynomial-based symbolic calculation offers a significant advancement in modeling complex sequential processes.
This approach enhances the expressiveness and applicability of Petri net models.
The method is particularly effective for systems requiring robust failure recovery mechanisms.