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 1.2 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Software Impacts Pub Date : 2025-03-01 Epub Date: 2024-12-27 DOI:10.1016/j.simpa.2024.100732
Y.P. Tsang , D.Y. Mo , K.T. Chung , C.K.M. Lee
{"title":"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","authors":"Y.P. Tsang ,&nbsp;D.Y. Mo ,&nbsp;K.T. Chung ,&nbsp;C.K.M. Lee","doi":"10.1016/j.simpa.2024.100732","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"23 ","pages":"Article 100732"},"PeriodicalIF":1.2000,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"DeepPack3D: A Python package for online 3D bin packing optimization by deep reinforcement learning and constructive heuristics\",\"authors\":\"Y.P. Tsang ,&nbsp;D.Y. Mo ,&nbsp;K.T. Chung ,&nbsp;C.K.M. Lee\",\"doi\":\"10.1016/j.simpa.2024.100732\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":29771,\"journal\":{\"name\":\"Software Impacts\",\"volume\":\"23 \",\"pages\":\"Article 100732\"},\"PeriodicalIF\":1.2000,\"publicationDate\":\"2025-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Software Impacts\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2665963824001209\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/12/27 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, SOFTWARE ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Software Impacts","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2665963824001209","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/12/27 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
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。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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
期刊最新文献
Keycloak-SSI: A Keycloak extension for SSI-based user-controlled attribute verification in federated identity management flows TETREES: Trade-off Evaluation Through Refined Exact Epsilon-Constraint Solver QUALITY: Quick Unified Automation Leveraging Intelligent Test Yield overhang_surrogates: A Python package for sampling, training and visualising surrogate models for building energy simulations Middleware-enforced Timed Causal Consistency for Apache Cassandra: An energy–performance–consistency evaluation against static consistency levels using YCSB
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1