DeepPack3D: A Python package for online 3D bin packing optimization by deep reinforcement learning and constructive heuristics

Y. P. Tsang (Corresponding Author), D. Y. Mo, K. T. Chung, C. K.M. Lee

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

The rapid advancement of industrial robotic automation has increased the significance of online 3D bin packing optimization for applications, like palletization and container loading. Despite numerous learning-based methods emerging for informed decision-making in this process, the absence of a standardized benchmark makes it challenging to experience the process and validate new algorithms. To bridge this gap, we introduce DeepPack3D, a software package that integrates deep reinforcement learning and constructive heuristic approaches for online 3D bin packing optimization. DeepPack3D provides a foundation for benchmarking, allowing users to evaluate performance using customizable item lists and lookahead values, thereby facilitating consistent research advancements.

Original languageEnglish
Article number100732
JournalSoftware Impacts
Volume23
DOIs
Publication statusPublished - Mar 2025

Keywords

  • 3D bin packing
  • Constructive heuristics
  • Deep reinforcement learning
  • Online optimization
  • Python

ASJC Scopus subject areas

  • Software

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