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The optimization problem based on alternatives aggregation Petri nets as models for industrial discrete event systems

机译:基于替代聚集Petri网作为工业离散事件系统模型的优化问题

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The construction, set-up and operation of many systems of interest in sectors such as industry, supply chains and communications are complex processes, which may require significant investment of resources. For this reason, the automation of the decision making for achieving the best design and operation of such systems, which may be regarded as discrete event systems (DESs), constitutes an active research field. In this paper, we present a methodology to cope with this process in an efficient way, optimizing not only the behaviour of the DES but also its structure. This kind of problem is usually associated with the so-called combinatorial explosion, since the number of alternative configurations for the DES might be huge. We present an improved algorithm to transform a set of alternative Petri nets, representing alternative structural configurations, into a more compact model called an alternatives aggregation Petri net. In real decision-making problems, where the different alternative structural configurations may share common subnets, this compact model may allow the development of a much more efficient optimization problem than the classic approach of 'divide and conquer'. The achievement of this objective is performed by developing a single and compact model for all of the alternative structural configurations of the DES and the simulation of the most promising of them. In this paper, the mentioned methodology is introduced and its advantages and drawbacks are described in relation with the classic approach.
机译:工业,供应链和通讯等行业中许多感兴趣的系统的构建,设置和操作是复杂的过程,可能需要大量的资源投资。由于这个原因,为实现这种系统的最佳设计和运行而进行的决策自动化是一个活跃的研究领域,可以将其视为离散事件系统(DES)。在本文中,我们提出了一种有效地应对这一过程的方法,不仅优化了DES的行为,而且优化了其结构。这种问题通常与所谓的组合爆炸有关,因为DES的替代配置数量可能很大。我们提出了一种改进的算法,可以将代表替代结构配置的一组替代Petri网转换为更紧凑的模型,称为替代聚合Petri网。在实际的决策问题中,不同的替代结构配置可以共享公共子网,这种紧凑的模型可以允许开发比“分而治之”的经典方法更为有效的优化问题。通过为DES的所有替代结构配置开发一个单一的紧凑模型并对其进行模拟,可以实现该目标。本文介绍了上述方法,并结合经典方法描述了其优缺点。

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