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A decomposition method for assembly/disassembly systems with blocking and general distributions
Flexible Services and Manufacturing Journal ( IF 2.7 ) Pub Date : 2019-01-17 , DOI: 10.1007/s10696-019-09332-z
Jean-Sébastien Tancrez

A modelling methodology is presented for assembly/disassembly systems with general processing time distributions and finite buffers. The approach combines the distributions discretization and a decomposition technique to analyze large manufacturing systems in a reasonable computational time and with good accuracy. In the decomposition technique, the system is decomposed into two station subsystems and the processing time distributions of the virtual stations are iteratively modified to approximate the impact of the rest of the network, adding estimations of the blocking and starving distributions. To analyze each subsystem, the general processing time distributions are discretized by aggregation of the probability masses, and the subsystem is then analytically modeled using a discrete Markov chain. We first show that this approach allows an accurate estimation of the subsystems cycle time distributions, which is crucial in the decomposition technique. Using computational experiments, we show that our decomposition method leads to accurate performance evaluation for large manufacturing systems (relative error on the order of 1%) and that the fine distribution estimation indeed seems to bring an improvement. Furthermore, we show on examples that, using decomposition, the cycle time distributions can be approximated reliably for large systems.

中文翻译:

具有分布和一般分布的组装/拆卸系统的分解方法

提出了一种具有通用处理时间分布和有限缓冲区的组装/拆卸系统建模方法。该方法结合了分布离散化和分解技术,可以在合理的计算时间内以良好的精度分析大型制造系统。在分解技术中,将系统分解为两个站点子系统,并对虚拟站点的处理时间分布进行迭代修改,以近似估计其余网络的影响,从而增加对阻塞和饥饿分布的估计。为了分析每个子系统,通过汇总概率质量离散化总体处理时间分布,然后使用离散的马尔可夫链对子系统进行分析建模。我们首先表明,这种方法可以准确估计子系统的循环时间分布,这在分解技术中至关重要。通过计算实验,我们证明了我们的分解方法可以对大型制造系统进行准确的性能评估(相对误差约为1%),并且精细分布估计的确可以带来改善。此外,我们在示例中显示,通过分解,对于大型系统,周期时间分布可以可靠地近似。我们表明,我们的分解方法可对大型制造系统进行准确的性能评估(相对误差为1%左右),精细分布估计的确可以带来改善。此外,我们在示例中显示,通过分解,对于大型系统,周期时间分布可以可靠地近似。我们表明,我们的分解方法可对大型制造系统进行准确的性能评估(相对误差为1%左右),精细分布估计的确可以带来改善。此外,我们在示例中显示,通过分解,对于大型系统,周期时间分布可以可靠地近似。
更新日期:2019-01-17
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