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

TL;DR

This paper presents a deep learning system designed to improve variable-sized bin packing by accurately predicting the best heuristic, leveraging large datasets and automatic feature selection for enhanced adaptive performance.

Contribution

It introduces a novel deep learning approach that overcomes traditional feature engineering limitations to select optimal heuristics for bin packing problems.

Findings

Achieves up to 89% training accuracy

Attains 72% validation accuracy in heuristic selection

Demonstrates effectiveness of deep learning in combinatorial optimization

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.

Full text

See pages 1-last of mao_conf.pdf