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Layout Optimization of Flexible Manufacturing Cells Based on Fuzzy Demand and Machine Flexibility

机译:基于模糊需求和机器柔性的柔性制造单元布局优化

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Layout flexibility is critical for the performance of flexible manufacturing cells, especially in dynamic production environment. To improve layout flexibility, layout optimization should consider more flexible factors based on existed models. On the one hand, not only should the current production demands be covered, but also the future uncertain demands should be considered so that the cell can adapt to the dynamic changes in a long term. On the other hand, the flexibility of machines should be balanced in the layout in order to guarantee that the cell can deal with dynamic new product introduction. Starting from these two points, we formulate a layout optimization model based on fuzzy demand and machine flexibility and then develop a genetic algorithm with bilayer chromosome to solve the model. We apply this new model to a flexible cell of shell products and test its performance by comparing it with the classical two-stage model. The total logistics path of the new model is shown to be significantly shorter than the classical model. Then we carry out adaptability experiments to test the flexibility of the new model. For the dynamic situation of both the fluctuation of production demands and the introduction of new products, the new model shows obvious advantages to the classical model. The results indicate that this advantage becomes greater as the dynamics becomes greater, which implies that considering fuzzy demand and machine flexibility is necessary and reasonable in layout optimization, especially when the dynamics of the production environment is dramatic.
机译:布局灵活性对于柔性制造单元的性能至关重要,尤其是在动态生产环境中。为了提高布局的灵活性,布局优化应基于现有模型考虑更多的灵活性因素。一方面,不仅应该满足当前的生产需求,而且还应该考虑未来的不确定需求,以便电池可以长期适应动态变化。另一方面,应在布局上平衡机器的灵活性,以确保单元可以处理动态的新产品引入。从这两点出发,我们基于模糊需求和机器灵活性建立了布局优化模型,然后开发了一种具有双层染色体的遗传算法来求解该模型。我们将此新模型应用于外壳产品的柔性单元,并通过与经典的两阶段模型进行比较来测试其性能。结果表明,新模型的总物流路径明显短于经典模型。然后我们进行适应性实验以测试新模型的灵活性。对于生产需求波动和新产品推出的动态情况,新模型显示出明显优于经典模型的优势。结果表明,随着动态变化的增加,这种优势也越来越大,这意味着在布局优化中考虑模糊需求和机器灵活性是必要且合理的,尤其是在生产环境的动态变化很大时。

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