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首页> 外文期刊>Simulation modelling practice and theory: International journal of the Federation of European Simulation Societies >Cross-entropy-based adaptive optimization of simulation parameters for Markovian-driven service systems
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Cross-entropy-based adaptive optimization of simulation parameters for Markovian-driven service systems

机译：马尔科夫驱动的服务系统基于交叉熵的仿真参数自适应优化

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摘要

Markov fluid models represent a general description of the process of service request arrivals to service systems. The solution of performance analysis problems incorporating them often calls for a simulation approach, for which a reference methodology is Importance Sampling. However, in this case the appropriate choice of the biasing conditions is a problem in itself. In this paper an iterative method based on the cross-entropy is proposed for this choice. The equations are given that allow to derive the biasing conditions from the simulation itself. The application of the proposed method to three different sample cases, referring to one transient scenario (finite time horizon and prescribed initial conditions) and two stationary cases, shows that the method is quite accurate and that during the path towards overflow the buffer fills mostly in the first phases.
机译：马尔可夫流体模型代表对服务请求到达服务系统的过程的一般描述。结合了性能分析问题的解决方案通常需要一种仿真方法，其参考方法是重要性抽样。然而，在这种情况下，偏压条件的适当选择本身就是一个问题。本文针对这种选择提出了一种基于交叉熵的迭代方法。给出了允许从模拟本身得出偏置条件的方程式。将该方法应用于三种不同的示例情况，并参考一种瞬时情况（有限的时间范围和规定的初始条件）和两种平稳情况，表明该方法非常准确，并且在向溢出的路径中，缓冲区主要填充了第一阶段。

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