Abstract
Predicting the performance of a computer, manufacturing, telecommunication, workflow, or transportation system is almost always a challenging task. Such a system usually comprises multiple activities or processes that proceed concurrently. In a typical computer workstation, for example, the storage subsystem writes data to a disk while, at the same time, one or more CPUs perform computations and a keyboard transmits characters to a buffer. Activities often have precedence relationships: assembly of a part in a manufacturing cell does not begin until assembly of each of its subparts has completed. Moreover, specified activities may be synchronized in that they must always start or terminate at the same time. Activities frequently compete for limited resources, and one activity may have either preemptive or nonpreemptive priority over another activity for use of a resource. To further complicate matters, many of the component processes of a system—such as the arrival process of calls to a telephone network—are random in nature. Because of this complexity and randomness, developing mathematical models of the system under study is usually nontrivial. The standard “network of queues” modelling framework, for example, can fail to capture complex synchronization behavior or precedence constraints. Assessment of system performance is equally difficult. Models that are accurate enough to adequately represent system behavior often cannot be analyzed using, for example, methods based on the theory of continuous-time Markov chains on a finite or countably infinite state space.
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© 2002 Springer Science+Business Media New York
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Haas, P.J. (2002). Introduction. In: Stochastic Petri Nets. Springer Series in Operations Research. Springer, New York, NY. https://doi.org/10.1007/0-387-21552-2_1
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DOI: https://doi.org/10.1007/0-387-21552-2_1
Publisher Name: Springer, New York, NY
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