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An iterative decomposition for asynchronous mixed-model assembly lines: combining balancing, sequencing, and buffer allocation

机译:异步混合模型装配线的迭代分解:组合平衡,排序和缓冲区分配

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摘要

Asynchronous Mixed-Model Assembly lines are common production layouts dedicated to large-scale manufacturing of similar products. Cyclically scheduling such products is an interesting strategy to obtain high and stable throughput. In order to best optimise these lines, it is necessary to combine line balancing, model sequencing, and buffer allocation. However, few works integrate these three degrees of freedom, and evaluating steady-state performance as a consequence of these decisions is challenging. This paper presents a mathematical model that allows an exact steady-state performance evaluation of these lines, and hence their optimisation. While the combination of degrees of freedom is advantageous, it is also computational costly. An iterative decomposition procedure based on alternation between two mathematical models and on optimality cuts is also presented. The decomposition is tested against the proposed mathematical model in a 700-instance dataset. The developed methods obtained 142 optimal answers. Results show that the decomposition outperforms the monolithic mathematical model, in particular for larger and harder instances in terms of solution quality. The optimality cuts are also shown to help the decomposition steps in terms of solution quality and time. Comparisons to a sequential procedure further demonstrate the importance of simultaneously optimising the three degrees of freedom, as both the proposed model and the decomposition outperformed such procedure.
机译:异步混合模型装配线是常用于类似产品的大规模制造的常见生产布局。循环安排此类产品是获得高且稳定的吞吐量的有趣策略。为了最佳优化这些行,有必要将线平衡,模型排序和缓冲区分配组合。然而,很少有效地整合这三项自由,并根据这些决定的结果评估稳态性能是具有挑战性的。本文提出了一种允许对这些线进行精确的稳态性能评估的数学模型,从而实现它们的优化。虽然自由度的组合是有利的,但它也是计算成本昂贵的。还提出了一种基于两个数学模型和最优性切割的交替的迭代分解过程。在700-instance数据集中针对所提出的数学模型测试分解。开发方法获得了142个最佳答案。结果表明,分解优于单片数学模型,特别是在溶液质量方面的较大和更难的情况。还显示最佳切割,以帮助分解步骤在溶液质量和时间方面。顺序过程的比较进一步证明了同时优化三个自由度的重要性,因为所提出的模型和分解优于这种程序。

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