随机Petri网的分解和压缩技术

ON THE DECOMPOSITION AND AGGREGATION FOR STOCHASTIC PETRI NETS

本文综述了在随机Petri网的分解和压缩技术方面的一些最近的工作，着重介绍了时间数量级分解、接近无关分解、响应时间保留压缩、流等价压缩、层次模型和分层分析与乘积形式解等技术的基本思路、方法和操作过程．本文也描述了解决系统模型状态空间爆炸问题所面临的困难和进一步的研究方向．

This is a review paper on decomposition and aggregation for stochastic Petri nets. This paper gives a brief review of the methodologies considered in the literature. These techniques include the time scale decomposition, the near-independent decomposition, the response tirne preservation, the flow equivalent aggregation, the hierarchical modeling and the product-form solution. A few directions of research are discussed at the end of the paper to outline the open problems that still need to be solved in this area.