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Concurrent generalized Petri nets (CGPNs) are a subclass of Markov
regenerative stochastic Petri nets, characterized by timed transitions
with general distributed firing times (GEN transitions), that become
enabled simultaneously while no other GEN transitions can be activated
during their enabling periods. In their original definition, CGPNs were...
Contexts in source publication
Context 1
... us first introduce the preliminary example of Fig.1 to informally emphasize the structural relations that may hold among transitions of a PN system (with priority) and of which we are interested in. ...
Context 2
... these structural relations, we will focus on conflicts and mutual exclusion. Conflicting transitions have at least one input place in common (struc- tural conflict), as it is the case of the pairs of transitions´Ø transitions´Ø Ø µ ´Ø Ø µ, and´Øand´Ø Ø µ of Fig.1, or such that the out- put set of one of them is not disjoint from the inhibitor set of another, as for the pair of transitions´Øtransitions´Ø ½ Ø ¿ µ. ...
Context 3
... instance, in the PN system of Fig.1, transitions´Ø transitions´Ø ½ Ø ¾ µ are mutually exclusive due to the È -semiflow whose support is the set of places´Ôplaces´Ô ½ Ô ¾ Ô Ô µ and in which the total amount of tokens in each marking reachable from the initial one is equal to one; transitions´Øtransitions´Ø ½ Ø ½¼ µ are mutually exclusive due to the inhibitor arc´Ôarc´Ô Ø ½ µ; finally, transi- tions´Øtions´Ø Ø µ are mutually exclusive due to transition Ø that shares their input places, but has a higher priority´½µpriority´priority´½µ. ...
Citations
... Observe that restrictions C1 and C2 can be verified by applying sufficient structural conditions, e.g., structural mutual exclusion condition based on P-invariants for C1 and structural marking bound computation for C2. Actually, similar conditions were formulated in [36] to identify a class of regenerative Stochastic Petri Nets without the generation of the state space. ...
Time Petri nets (TPNs) have been widely used for the verification and validation of real-time systems during the software development process. Their quantitative analysis consists in applying enumerative techniques that suffer the well known state space explosion problem. To overcome this problem, several methods have been proposed in the literature, that either provide rules to obtain equivalent nets with a reduced state space or avoid the construction of the whole state space. In this paper, we propose a method that consists in computing performance bounds to predict the average operational behavior of TPNs by exploiting their structural properties and by applying operational laws. Performance bound computation was first proposed for timed (Timed PNs) and stochastic Petri nets (SPNs). We generalize the results obtained for Timed PNs and SPNs to make the technique applicable to TPNs and their extended stochastic versions: TPN with firing frequency intervals (TPNFs) and extended TPNs (XTPNs). Finally, we apply the proposed bounding techniques on the case study of a robot-control application taken from the literature.
Availability of a common, shared benchmark to provide repeatable, quantifiable, and comparable results is an added value for any scientific community. International consortia provide benchmarks in a wide range of domains, being normally used by industry, vendors, and researchers for evaluating their software products. In this regard, a benchmark of untimed Petri net models was developed to be used in a yearly software competition driven by the Petri net community. However, to the best of our knowledge there is not a similar benchmark to evaluate solution techniques for Petri nets with timing extensions. In this paper, we propose an evaluation framework for the comparative analysis of generalized stochastic Petri nets (GSPNs) simulation techniques. Although we focus on simulation techniques, our framework provides a baseline for a comparative analysis of different GSPN solvers (e.g., simulators, numerical solvers, or other techniques). The evaluation framework encompasses a set of 50 GSPN models including test cases and case studies from the literature, and a set of evaluation guidelines for the comparative analysis. In order to show the applicability of the proposed framework, we carry out a comparative analysis of steady-state simulators implemented in three academic software tools, namely, GreatSPN, PeabraiN, and TimeNET. The results allow us to validate the trustfulness of these academic software tools, as well as to point out potential problems and algorithmic optimization opportunities.
