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A comparative study of production control mechanisms using simulation-based multi-objective optimisation

机译:基于仿真的多目标优化生产控制机制的比较研究

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There exist many studies conducted to compare the performance of different production control mechanisms (PCMs) in order to determine which one performs the best under different conditions. Nonetheless, most of these studies suffer from the problems that the PCMs are not compared with their optimal parameter settings in a truly multi-objective context. This paper describes how different PCMs can be compared under their optimal settings through generating the Pareto-optimal frontiers, in the form of optimal trade-off curves in the performance space, by applying evolutionary multi-objective optimisation to simulation models. This concept is illustrated with a bi-objective comparative study of the four most popular PCMs in the literature, namely Push, Kanban, CONWIP and DBR, on an unbalanced serial flow line in which both control parameters and buffer capacities are to be optimised. Additionally, it introduces the use of normalised hyper-volume as the quantitative metric and confidence-based significant dominance as the statistical analysis method to verify the differences of the PCMs in the performance space. While the results from this unbalanced flow line cannot be generalised, it indicates clearly that a PCM may be preferable in certain regions of the performance space, but not others, which supports the argument that PCM comparative studies have to be performed within a Pareto-based multi-objective context.
机译:为了比较不同生产控制机制(PCM)的性能,进行了许多研究,以确定哪一种在不同条件下性能最佳。但是,这些研究大多数都存在以下问题:在真正的多目标环境中,PCM无法与其最优参数设置进行比较。本文描述了如何通过在性能空间中以最佳折衷曲线的形式生成帕累托最优边界,并通过将进化多目标优化应用于仿真模型,从而在最佳设置下比较不同的PCM。通过在不平衡的串行流线上对文献中四种最流行的PCM(即Push,Kanban,CONWIP和DBR)进行双目标比较研究,说明了这一概念,在该流水线中,控制参数和缓冲容量都将得到优化。此外,它介绍了使用归一化超容量作为量化指标,以及基于置信度的显着优势作为统计分析方法,以验证性能空间中PCM的差异。尽管无法概括这种不平衡流线的结果,但它清楚地表明,PCM在性能空间的某些区域中可能更可取,但在其他区域则不然,这支持以下观点:PCM比较研究必须在基于帕累托的基础上进行多目标环境。

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