ACM Transactions on Computer Systems (TOCS)
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Markov Decision Processes: Discrete Stochastic Dynamic Programming
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ACM SIGMETRICS Performance Evaluation Review - Special issue on Stochastic Petri Nets
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ICATPN '01 Proceedings of the 22nd International Conference on Application and Theory of Petri Nets
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An Introduction to the Theoretical Aspects of Coloured Petri Nets
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On Non-Decidability of Reachability for Timed-Arc Petri Nets
PNPM '99 Proceedings of the The 8th International Workshop on Petri Nets and Performance Models
Model Checking of Time Petri Nets Using the State Class Timed Automaton
Discrete Event Dynamic Systems
Introduction to Probability Models, Ninth Edition
Introduction to Probability Models, Ninth Edition
Performance Evaluation of Asynchronous Concurrent Systems Using Petri Nets
IEEE Transactions on Software Engineering
Formal Aspects of Computing
The temporal logic of programs
SFCS '77 Proceedings of the 18th Annual Symposium on Foundations of Computer Science
On the Compared Expressiveness of Arc, Place and Transition Time Petri Nets
Fundamenta Informaticae - PETRI NETS 2007
Undecidability Results for Timed Automata with Silent Transitions
Fundamenta Informaticae
Structural Translation from Time Petri Nets to Timed Automata
Electronic Notes in Theoretical Computer Science (ENTCS)
Bounded Parametric Verification for Distributed Time Petri Nets with Discrete-Time Semantics
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
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In this paper we discuss several approaches to time in Petri nets. If time is considered for performance analysis, probability distributions for choices should be included into the model and thus we need Petri nets with time and stochastics. In literature, most attention is paid to models where the time is expressed by delaying transitions and for the stochastic case to continuous time models with exponential enabling distributions, known by its software tools as GSPN. Here we focus on discrete models where the time is expressed by delaying tokens and the probability distributions are discrete, because this model class has some advantages. We show how model checking methods can be applied for the non-stochastic case. For the stochastic case we show how Markov techniques can be used. We also consider structural analysis techniques, which do not need the state space.