The article puts forward the new layout methodology of the multi-floor linear cellular manufacturing layout. The proposed equipment layout methodology not just breaks the conventional single-floor linear cellular manufacturing layout but also meets the layout requirements of the intelligent manufacturing workshop for the stereoscopic aisle manufacturing cell. The layout methodology takes into account the least space occupation as well as the shortest total distance of logistics as the objective function, besides considering the limitations that exist between the equipment, different planes, different levels, and so on; also, a mathematical model is put forward. The multi-floor linear cellular manufacturing layout is solved based on the self-adapting multi-objective fruit fly optimization algorithm that refers to an algorithm combining fruit fly optimization algorithm and NSGA-II. Self-adapting multi-objective fruit fly optimization algorithm makes use of the fast nondominated sorting for the multi-target food concentration calculation, together with designing the adaptive olfactory search and visual search, and employing the perturbation operations for flight strategies, aimed at ensuring the population diversity. Simulation cases suggest that self-adapting multi-objective fruit fly optimization algorithm has stronger advantages as compared with multi-objective fruit fly algorithm and elitist non-dominated sorting genetic algorithm (NSGA-II) in the solution of multi-floor linear cellular manufacturing layout problems. The final engineering case application sheds light on the fact that multi-floor linear cellular manufacturing layout saves 57.6% of the area, in addition to 23.7% of space, and 29.2% of the handling distance as compared with single-floor linear cellular manufacturing layout. Accordingly, multi-floor linear cellular manufacturing layout has a specific reference value in the layout of facilities in the intelligent manufacturing plants.

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一种新的蜂窝制造布局：多层线性蜂窝制造布局

文章提出了多层线性蜂窝制造布局的新布局方法。所提出的设备布局方法不仅打破了传统的单层线性单元制造布局，而且满足了智能制造车间对立体通道制造单元的布局要求。布局方法除了考虑设备之间、不同平面、不同层次等存在的限制外，还以最少的空间占用和最短的物流总距离为目标函数；此外，还提出了一个数学模型。基于自适应多目标果蝇优化算法解决了多层线性蜂窝制造布局，该算法是指将果蝇优化算法与NSGA-II相结合的算法。自适应多目标果蝇优化算法利用快速非支配排序进行多目标食物浓度计算，并设计自适应嗅觉搜索和视觉搜索，并采用扰动操作进行飞行策略，旨在确保人口多样性。仿真案例表明自适应多目标果蝇优化算法在解决多层线性蜂窝制造布局问题上比多目标果蝇算法和精英非支配排序遗传算法（NSGA-II）具有更强的优势问题。最后的工程案例应用表明，与单层线性蜂窝制造布局相比，多层线性蜂窝制造布局节省了57.6%的面积、23.7%的空间和29.2%的搬运距离. 因此，多层线性蜂窝制造布局在智能制造工厂的设施布局中具有特定的参考价值。最后的工程案例应用表明，与单层线性蜂窝制造布局相比，多层线性蜂窝制造布局节省了57.6%的面积、23.7%的空间和29.2%的搬运距离. 因此，多层线性蜂窝制造布局在智能制造工厂的设施布局中具有特定的参考价值。最后的工程案例应用表明，与单层线性蜂窝制造布局相比，多层线性蜂窝制造布局节省了57.6%的面积、23.7%的空间和29.2%的搬运距离. 因此，多层线性蜂窝制造布局在智能制造工厂的设施布局中具有特定的参考价值。