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Poisson Simulation as an Extension of Continuous System Simulation for the Modeling of Queuing Systems

机译:泊松仿真作为排队系统建模的连续系统仿真的扩展

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Poisson simulation is an extension of continuous system simulation whereby randomness is modeled as opposed to just adding noise. This article treats how Poisson simulation can be used for modeling queuing systems. The focus is on the implementation of queues in Poisson simulation and the connections to queuing theory. This approach also has theoretical and practical implications. Dynamic and stochastic systems, especially when queues are involved, are often treated by discrete event simulation using a microscopic view in which individual entities are modeled. Poisson simulation makes it possible to handle many such systems on a macroscopic level using aggregated states. It is therefore interesting to compare these approaches. Parallel approaches can then be sketched with discrete event simulationi in one branch and Poisson simulation in the other. A fundamental difference between the approaches is whether one prefers to based a model on individual, distinguishable entities or on lumped entities.
机译:泊松仿真是连续系统仿真的扩展,通过仿真可以对随机性进行建模,而不仅仅是增加噪声。本文讨论如何将Poisson仿真用于排队系统建模。重点是泊松仿真中队列的实现以及与排队论的联系。这种方法也具有理论和实践意义。动态和随机系统,尤其是涉及队列的系统,通常是通过使用单个实体建模的微观视图通过离散事件模拟来处理的。泊松仿真使使用聚合状态在宏观上处理许多此类系统成为可能。因此,比较这些方法很有趣。然后,可以在一个分支中使用离散事件模拟i,在另一个分支中使用泊松模拟来绘制并行方法。这些方法之间的根本区别在于,是希望将模型建立在单独的,可区分的实体上还是集总实体上。

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