Title: Small Boxes Big Data: A Deep Learning Approach to Optimize Variable Sized Bin Packing,
Abstract: 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.