一种基于学习的两阶段多线程迭代贪心算法用于序列依赖的分布式工厂和自动导引车协同调度

IF 5.3 3区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Zijiang Liu;Hongyan Sang;Biao Zhang;Leilei Meng;Tao Meng
{"title":"一种基于学习的两阶段多线程迭代贪心算法用于序列依赖的分布式工厂和自动导引车协同调度","authors":"Zijiang Liu;Hongyan Sang;Biao Zhang;Leilei Meng;Tao Meng","doi":"10.1109/TETCI.2025.3540405","DOIUrl":null,"url":null,"abstract":"Automated guided vehicles are widely utilized in the real production environment for tasks such as job transfer and inter-factory collaboration, yet they remain relatively underexplored in academic research. This study addresses the distributed permutation flow shop co-scheduling problem with sequence-dependent setup times (DPFCSP-SDST). We propose a novel solution that leverages an optimization algorithm, specifically a learning-based two-stage multi-thread iterated greedy algorithm (LTMIG). First, a problem-specific initialization method is designed to generate the initialization solution in two stages. Second, a Q-learning-based operator adaptation strategy is adopted to guide the evolutionary direction of factory assignment to reduce the makespan. Then, the proposed destructive-construction strategy builds an archive set to share historical knowledge with different stages of search, ensuring exploration capability. Local search effectively combines the parallel computing power of multi-threading with the inherent exploitation capability of LTMIG, and fully utilizes the information of elite solutions. Extensive experimental results demonstrate that LTMIG is significantly better than the comparison algorithms mentioned in the paper, and it turns out that LTMIG is the most suitable algorithm for solving DPFCSP-SDST.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 3","pages":"2208-2218"},"PeriodicalIF":5.3000,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Learning-Based Two-Stage Multi-Thread Iterated Greedy Algorithm for Co-Scheduling of Distributed Factories and Automated Guided Vehicles With Sequence-Dependent Setup Times\",\"authors\":\"Zijiang Liu;Hongyan Sang;Biao Zhang;Leilei Meng;Tao Meng\",\"doi\":\"10.1109/TETCI.2025.3540405\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Automated guided vehicles are widely utilized in the real production environment for tasks such as job transfer and inter-factory collaboration, yet they remain relatively underexplored in academic research. This study addresses the distributed permutation flow shop co-scheduling problem with sequence-dependent setup times (DPFCSP-SDST). We propose a novel solution that leverages an optimization algorithm, specifically a learning-based two-stage multi-thread iterated greedy algorithm (LTMIG). First, a problem-specific initialization method is designed to generate the initialization solution in two stages. Second, a Q-learning-based operator adaptation strategy is adopted to guide the evolutionary direction of factory assignment to reduce the makespan. Then, the proposed destructive-construction strategy builds an archive set to share historical knowledge with different stages of search, ensuring exploration capability. Local search effectively combines the parallel computing power of multi-threading with the inherent exploitation capability of LTMIG, and fully utilizes the information of elite solutions. Extensive experimental results demonstrate that LTMIG is significantly better than the comparison algorithms mentioned in the paper, and it turns out that LTMIG is the most suitable algorithm for solving DPFCSP-SDST.\",\"PeriodicalId\":13135,\"journal\":{\"name\":\"IEEE Transactions on Emerging Topics in Computational Intelligence\",\"volume\":\"9 3\",\"pages\":\"2208-2218\"},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2025-03-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Emerging Topics in Computational Intelligence\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10925871/\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Emerging Topics in Computational Intelligence","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10925871/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

自动导引车在实际生产环境中被广泛应用于工作转移和工厂间协作等任务,但在学术研究中相对较少。本文研究了具有序列相关设置时间(DPFCSP-SDST)的分布式排列流水车间协同调度问题。我们提出了一种利用优化算法的新解决方案,特别是基于学习的两阶段多线程迭代贪婪算法(LTMIG)。首先,设计了针对具体问题的初始化方法,分两个阶段生成初始化解。其次,采用基于q学习的算子自适应策略来引导工厂分配的演化方向,以减小完工时间;然后,本文提出的破坏性构建策略构建了一个档案集,在不同的搜索阶段共享历史知识,保证了探索能力。局部搜索有效地结合了多线程的并行计算能力和LTMIG固有的开发能力,充分利用了精英解的信息。大量的实验结果表明,LTMIG明显优于文中提到的比较算法,LTMIG是最适合求解DPFCSP-SDST的算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Learning-Based Two-Stage Multi-Thread Iterated Greedy Algorithm for Co-Scheduling of Distributed Factories and Automated Guided Vehicles With Sequence-Dependent Setup Times
Automated guided vehicles are widely utilized in the real production environment for tasks such as job transfer and inter-factory collaboration, yet they remain relatively underexplored in academic research. This study addresses the distributed permutation flow shop co-scheduling problem with sequence-dependent setup times (DPFCSP-SDST). We propose a novel solution that leverages an optimization algorithm, specifically a learning-based two-stage multi-thread iterated greedy algorithm (LTMIG). First, a problem-specific initialization method is designed to generate the initialization solution in two stages. Second, a Q-learning-based operator adaptation strategy is adopted to guide the evolutionary direction of factory assignment to reduce the makespan. Then, the proposed destructive-construction strategy builds an archive set to share historical knowledge with different stages of search, ensuring exploration capability. Local search effectively combines the parallel computing power of multi-threading with the inherent exploitation capability of LTMIG, and fully utilizes the information of elite solutions. Extensive experimental results demonstrate that LTMIG is significantly better than the comparison algorithms mentioned in the paper, and it turns out that LTMIG is the most suitable algorithm for solving DPFCSP-SDST.
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
CiteScore
10.30
自引率
7.50%
发文量
147
期刊介绍: The IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI) publishes original articles on emerging aspects of computational intelligence, including theory, applications, and surveys. TETCI is an electronics only publication. TETCI publishes six issues per year. Authors are encouraged to submit manuscripts in any emerging topic in computational intelligence, especially nature-inspired computing topics not covered by other IEEE Computational Intelligence Society journals. A few such illustrative examples are glial cell networks, computational neuroscience, Brain Computer Interface, ambient intelligence, non-fuzzy computing with words, artificial life, cultural learning, artificial endocrine networks, social reasoning, artificial hormone networks, computational intelligence for the IoT and Smart-X technologies.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信