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 language | English |
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Article number | 100732 |
Journal | Software Impacts |
Volume | 23 |
DOIs | |
Publication status | Published - Mar 2025 |
Keywords
- 3D bin packing
- Constructive heuristics
- Deep reinforcement learning
- Online optimization
- Python
ASJC Scopus subject areas
- Software