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This kind of invariance characterizes the weak lumpability property of Markov chains. As an example, well-formed nets (see Part 1) generate Markov chains with invariant cones of which the extremal ray is the vector 1 = (1, ,1). The dimension of the vector 1 is determined by the number of states of the aggregate we consider. Based on techniques developed in [LED 01] we can bound the initial Markov chain with a large state space by bounding chains with fewer numbers of states. These techniques are based on positive invariance and the stochastic order called strong ordering. All these bounding techniques lead to numerical algorithms to bound the transient and the stationary behavior of the Markov chain with a large state space. These techniques also provide a bound on the error made. However, the major drawback of such methodologies is that they can lead to large errors. 18.3.1.4. Analytical methods Analytical methods for performance evaluation of manufacturing systems developed in [DAL 92] are a means of avoiding simulation. These methods are based on the state recursion equation of the system and the Markovian assumption. rdlc ean 13 EAN - 13 Client Report RDLC Generator | Using free sample for EAN ...
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