Machine Learning for the Multi-Dimensional Bin Packing Problem: Literature Review and Empirical Evaluation

TL;DR

This paper reviews the application of machine learning techniques to the multi-dimensional bin packing problem, including a survey of existing methods, benchmark evaluations, and future research directions.

Contribution

It provides the first systematic review of ML methods for BPP, along with empirical evaluation on benchmark datasets and discussion of challenges.

Findings

Evaluated online methods on Cutting Stock Dataset

Collected and analyzed public benchmarks for 3D BPP

Identified key challenges and future directions in ML for BPP

Abstract

The Bin Packing Problem (BPP) is a well-established combinatorial optimization (CO) problem. Since it has many applications in our daily life, e.g. logistics and resource allocation, people are seeking efficient bin packing algorithms. On the other hand, researchers have been making constant advances in machine learning (ML), which is famous for its efficiency. In this article, we first formulate BPP, introducing its variants and practical constraints. Then, a comprehensive survey on ML for multi-dimensional BPP is provided. We further collect some public benchmarks of 3D BPP, and evaluate some online methods on the Cutting Stock Dataset. Finally, we share our perspective on challenges and future directions in BPP. To the best of our knowledge, this is the first systematic review of ML-related methods for BPP.