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Algorithms for the generation of state-level representations of stochastic activity networks with general reward structures.

Qureshi, M.A.; Sanders, W.H.; van Moorsel, A.P.A.; German, R.

In: IEEE Computer Soc. Press, Proc. of 6th International Workshop on Petri Nets and Performance Models - PNPM'95, Durham, N. Carolina, USA, pages 180-190. 1995.

Also in: IEEE Transactions on Software Engineering, pages 603-614. September 1996.

Abstract: Stochastic Petri nets and extensions are a popular method for evaluating a wide variety of systems. In most cases, their numerical solution requires generating a state-level stochastic process, which captures the behavior of the SPN with respect to a set of specified performance measures. These measures are commonly defined at the net level by means of a reward variable. In this paper, we discuss issues regarding the generation of state-level reward models for systems specified as stochastic activity networks with step-based reward structures.

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