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Bin Packing problems have been widely studied because of their broadapplications in different domains. Known as a set of NP-hard problems, theyhave different vari- ations and many heuristics have been proposed forobtaining approximate solutions. Specifically, for the 1D variable sized binpacking problem, the two key sets of optimization heuristics are the binassignment and the bin allocation. Usually the performance of a single staticoptimization heuristic can not beat that of a dynamic one which is tailored foreach bin packing instance. Building such an adaptive system requires modelingthe relationship between bin features and packing perform profiles. The primarydrawbacks of traditional AI machine learnings for this task are the naturallimitations of feature engineering, such as the curse of dimensionality andfeature selection quality. We introduce a deep learning approach to overcomethe drawbacks by applying a large training data set, auto feature selection andfast, accurate labeling. We show in this paper how to build such a system byboth theoretical formulation and engineering practices. Our prediction systemachieves up to 89% training accuracy and 72% validation accuracy to select thebest heuristic that can generate a better quality bin packing solution.

Small Boxes Big Data: A Deep Learning Approach to Optimize Variable Sized Bin Packing