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首页> 外文期刊>The Journal of the Textile Institute >Evolutionary hyper-heuristic for solving the strip-packing problem
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Evolutionary hyper-heuristic for solving the strip-packing problem

机译：进化超启发式求解条带包装问题

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Strip-packing problem (marker making) is an optimization problem, where a set of cutting parts needs to be placed on a marker so that the items do not overlap, and do not exceed the boundaries of a marker. In this research a novel Grid algorithm is introduced, and improvement methods: Grid-BLP and Grid-Shaking. These algorithms were combined with genetic algorithm, and a novel placement order All equal first. An individual representation of a genetic algorithm has been developed that is consisted of placement sequence, rotation of a cutting part, the choice of a placement algorithm, and dynamic grid parameter. Experiments were conducted to determine the best placement algorithm for a dataset, and hyper-heuristic efficiency. The implementation has been developed and experiments were conducted in MATLAB using GEATbx toolbox on five datasets from textile industry: ALBANO, DAGLI, MAO, MARQUES and MAN SHIRT. The marker efficiency in percentage was recorded with best results: 85.17, 81.76, 78.67, 84.67 and 87.19% obtained for the datasets, respectively.
机译：条形包装问题（标记制作）是一种优化问题，其中需要将一组切割部件放置在标记上，使得物品不重叠，并且不超过标记的边界。在本研究中，介绍了一种新颖的网格算法，改进方法：电网 - BLP和摇动。这些算法与遗传算法相结合，并且新颖的放置顺序首先相等。已经开发了一种遗传算法的单个表示，其由放置序列，切割部分的旋转，放置算法的选择和动态网格参数组成。进行实验以确定数据集的最佳放置算法，以及超启发式效率。已经开发了实施，并在纺织业的五个数据集上使用Geatbx工具箱在Matlab中进行了实验：阿尔巴诺，Dagli，毛，Marques和男士衬衫。百分比上的标记效率记录了最佳结果：85.17,81.76,78.67,84.67和87.19％分别为数据集获得。

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