DeepPack3D:一个Python包,通过深度强化学习和建设性启发式进行在线3D装箱优化

IF 1.3 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Y.P. Tsang , D.Y. Mo , K.T. Chung , C.K.M. Lee
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引用次数: 0

摘要

工业机器人自动化的快速发展,增加了在线3D装箱优化应用的重要性,如托盘和集装箱装载。尽管在此过程中出现了许多基于学习的方法来进行明智的决策,但由于缺乏标准化的基准,因此很难体验该过程并验证新算法。为了弥补这一差距,我们引入了DeepPack3D,这是一个集成了深度强化学习和建设性启发式方法的软件包,用于在线3D装箱优化。DeepPack3D为基准测试提供了基础,允许用户使用可定制的项目列表和前瞻性值来评估性能,从而促进一致的研究进展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
Software Impacts
Software Impacts Software
CiteScore
2.70
自引率
9.50%
发文量
0
审稿时长
16 days
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