小盒子大数据：一种优化可变尺寸装箱的深度学习方法
Small Boxes Big Data: A Deep Learning Approach to Optimize Variable Sized Bin Packing
摘要：
装箱问题因其在不同领域的广泛应用而得到了广泛的研究。它们被称为一组NP难问题，具有不同的变化，并且已经提出了许多算法来获得近似解。具体而言，对于一维可变尺寸装箱问题，优化算法的两个关键集是箱分配和箱分配。通常情况下，单一的静态优化启发式算法的性能不能优于动态优化启发式算法，动态优化启发式算法是为每个装箱实例定制的。建立这样一个自适应系统，需要建模之间的关系箱功能和包装执行配置文件。传统AI机器学习的主要缺点是特征工程的自然限制，例如维数灾难和特征选择质量。我们引入了一种深度学习方法，通过应用大型训练数据集、自动特征选择和快速准确的标记来克服这些缺点。我们在本文中展示了如何建立这样一个系统的理论公式和工程实践。我们的预测系统实现了高达89%的训练准确率和72%的验证准确率，以选择最佳启发式算法，从而生成质量更好的装箱解决方案。
英文摘要：
Bin Packing problems have been widely studied because of their broad applications in different domains. Known as a set of NP-hard problems, they have different vari- ations and many heuristics have been proposed for obtaining approximate solutions. Specifically, for the 1D variable sized bin packing problem, the two key sets of optimization heuristics are the bin assignment and the bin allocation. Usually the performance of a single static optimization heuristic can not beat that of a dynamic one which is tailored for each bin packing instance. Building such an adaptive system requires modeling the relationship between bin features and packing perform profiles. The primary drawbacks of traditional AI machine learnings for this task are the natural limitations of feature engineering, such as the curse of dimensionality and feature selection quality. We introduce a deep learning approach to overcome the drawbacks by applying a large training data set, auto feature selection and fast, accurate labeling. We show in this paper how to build such a system by both theoretical formulation and engineering practices. Our prediction system achieves up to 89% training accuracy and 72% validation accuracy to select the best heuristic that can generate a better quality bin packing solution.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
