DeepPack3D: A Python package for online 3D bin packing optimization by deep reinforcement learning and constructive heuristics
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. • DeepPack3D is an open-source Python package for online 3D bin packing optimization. • Deep Q network and four different constructive heuristics are supported. • Lookahead value can be customized to support a flexible online optimization process.
Authors
- C.K.M. Lee (ORCID: https://orcid.org/0000-0001-8577-4547)
- Yung Po Tsang (ORCID: https://orcid.org/0000-0002-6128-345X)
- Daniel Y. Mo (ORCID: https://orcid.org/0000-0001-8307-9065)
- K.T. Chung
Institutions
- Hong Kong Polytechnic University (HK)
- Hang Seng University of Hong Kong (HK)
- University of Hong Kong (HK)
Publication Details
- Journal
- Software Impacts
- Published
- 2024-12-27
- DOI
- https://doi.org/10.1016/j.simpa.2024.100732
- Citations
- 1
- Primary Topic
- Optimization and Packing Problems
- Type
- article
- Field-Weighted Citation Impact
- 0.35
Funders
- Hong Kong Polytechnic University