论文标题
ML可以预测困难组合问题的解决方案值吗？
Can ML predict the solution value for a difficult combinatorial problem?
论文作者
论文摘要
我们研究机器学习是否可以从输入中预测困难组合优化问题的最终目标函数值。我们的背景是减少模式问题，这是切割库存问题的工业重要但困难的方面。机器学习似乎比幼稚的模型具有更高的预测准确性，将平均绝对百分比误差（MAPE）从12.0％降低到8.7％。
We look at whether machine learning can predict the final objective function value of a difficult combinatorial optimisation problem from the input. Our context is the pattern reduction problem, one industrially important but difficult aspect of the cutting stock problem. Machine learning appears to have higher prediction accuracy than a naïve model, reducing mean absolute percentage error (MAPE) from 12.0% to 8.7%.
