Dataset for machine learning based approximations of strip packing / nesting heights

Solving strip packing or nesting problems with highly irregular shapes is a complex and computation time intensive task. In some cases, it is adequate to estimate the strip height instead of solving the nesting problem completely. The file Nesting sample data.csv/*.xlsx consists of 88,200 (complex) nesting instances generated and used in the paper "Approximate anticipation of base-level reactions by machine learning techniques used to substitute the solving of complex nesting problems". The first 14 columns describe details of the nesting instances (e.g., item types used in the instance, the width of the object, used in which phase of the machine learning process etc.), whereat the column "Nesting Solution" is the computed height of the instance. All the remaining columns are numerical features which can be used as a input in the machine learning process (for details see the reference paper). Besides the two data files, a jupyter notebook is provided to illustrate the usage of the data.求解具有高度不规则形状的条带排样（strip packing）与嵌套排样（nesting）问题，是一项复杂度极高且计算密集的任务。在部分应用场景中，无需完全求解嵌套排样问题，仅通过估算条带高度即可满足需求。文件Nesting样本数据.csv/*.xlsx包含88200个（复杂）嵌套排样实例，这些实例生成自论文《借助机器学习技术近似预估基元反应以替代复杂嵌套排样问题的求解》并用于该研究。该数据文件的前14列用于描述嵌套排样实例的详细信息（例如，实例中使用的工件类型、工件宽度、机器学习流程所处的阶段等），其中名为“Nesting Solution（嵌套排样解）”的列对应该实例的计算所得条带高度。剩余所有列均为可用于机器学习流程的数值特征（详细信息请参阅所引用的论文）。除上述两个数据文件外，本数据集还附带了一份Jupyter Notebook，用于演示该数据的使用方法。