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Simulation-based optimization of discrete event systems with alternative structural configurations using distributed computation and the Petri net paradigm

机译:使用分布式计算和Petri网范式的具有替代结构配置的离散事件系统的基于仿真的优化

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Decision-making on discrete event systems with alternative structural configurations is a field with application to the efficient design and operation of many systems, ranging from manufacturing facilities to communication networks. The solution of this problem may be afforded by its transformation into an optimization problem. A variety of statements for this optimization problem can be presented by using different formalisms able to describe the model of the system. These different statements allow developing diverse optimization algorithms for solving the problem, which may be very demanding for a computer. In this paper, several approaches are presented in order to reduce the computing requirements needed by the mentioned algorithms, some of them are implemented in one processor and others are based on distributed computing. In particular, this paper presents a new distributed methodology, which associates sets of alternative structural configurations of the system to different alternative aggregation Petri net (AAPNs), regarding the number of available processors. Under certain conditions, this methodology alleviates the computational requirements for every processor and speeds up the optimization process. A case-study is presented and different techniques are applied to solve it, for illustrating diverse distributed and non-distributed methodologies, regarding the available processors, as well as for comparing their relative performance.
机译:具有替代结构配置的离散事件系统的决策是一个应用领域,涉及从制造设施到通信网络的许多系统的有效设计和操作。该问题的解决方案可以通过将其转化为优化问题来提供。通过使用能够描述系统模型的不同形式主义,可以给出有关此优化问题的各种陈述。这些不同的陈述允许开发用于解决问题的各种优化算法,这可能对计算机非常有要求。在本文中,为了减少上述算法所需的计算需求,提出了几种方法,其中一些方法在一个处理器中实现,其他方法则基于分布式计算。特别是,本文提出了一种新的分布式方法,该方法将系统的替代结构配置集与不同的替代聚合Petri网(AAPN)关联,这取决于可用处理器的数量。在某些条件下,这种方法减轻了每个处理器的计算要求,并加快了优化过程。提出了一个案例研究,并应用了不同的技术来解决该问题,以说明各种分布式和非分布式方法,有关可用处理器以及比较它们的相对性能。

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