Exploiting Isomorphisms and Special Structures in the Analysis of Markov Regenerative Stochastic Petri Nets

Christoph Lindemann
Additional contact information
Christoph Lindemann: Architecture and Software Technology (GMD-FIRST) at the Technical University of Berlin, GMD Research Institute for Computer

Chapter 22 in Computations with Markov Chains, 1995, pp 383-402 from Springer

Abstract: Abstract We introduce a refined algorithm for determining the transition probability matrix of the embedded Markov chain underlying a Markov Regenerative Stochastic Petri Net (MRSPN), which continues previous work on improving the efficiency of the numerical solution method for MRSPNs. By observing that the digraph corresponding to the Markov chains subordinated to timed transitions with non-exponentially distributed firing delays of a MRSPN constitute a forest, we show how isomorphisms between its connected components can be exploited in order to reduce the computation time of their transient analysis. Furthermore, special structures for subordinated Markov chains are introduced for which the computational effort of the transient analysis can also be reduced. The computational benefit of the proposed approach is illustrated by four MRSPNs taken from the literature.

Keywords: Isomorphism Class; Transition Probability Matrix; Transient Analysis; Embed Markov Chain; Deterministic Transition (search for similar items in EconPapers)
Date: 1995
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4615-2241-6_22

Ordering information: This item can be ordered from
http://www.springer.com/9781461522416

DOI: 10.1007/978-1-4615-2241-6_22

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().