Academic Journal
Online simulation task scheduling in cloud manufacturing with cross attention and deep reinforcement learning.
| Title: | Online simulation task scheduling in cloud manufacturing with cross attention and deep reinforcement learning. |
|---|---|
| Authors: | Chen, Zhen, Zhang, Lin, Laili, Yuanjun, Wang, Xiaohan, Wang, Fei |
| Source: | Journal of Intelligent Manufacturing; Dec2025, Vol. 36 Issue 8, p5779-5800, 22p |
| Subject Terms: | Reinforcement learning, Scheduling, Digital computer simulation, Manufacturing industries, Automated planning & scheduling |
| Abstract: | Online simulation task scheduling in a private cloud manufacturing platform usually requires rapid decision-making algorithms because of the characteristics of unpredictability and diversity of tasks. However, the existing approaches face challenges in generating satisfactory scheduling schemes within a limited solving time. Therefore, this paper proposes a dynamic scheduling algorithm for online simulation task scheduling that is based on cross-attention and deep reinforcement learning (DRL). A multichannel DRL-based framework with discrete event triggering is introduced to effectively recognize online scheduling environments. An innovative multistep state feature cross-attention method is proposed to address the challenge of temporal features caused by nonsimultaneous task arrivals. A case study in the semiconductor display industry with 35 diverse scheduling scenarios was conducted to evaluate the efficacy of the proposed algorithm, which was compared with six classic state-of-the-art DRL algorithms and three commonly used priority dispatching rules. The results show that the proposed algorithm maintains superior scheduling performance across multiple scheduling scenarios and outperforms the other algorithms by an average of nearly 30% when the optimization objective is considered. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Intelligent Manufacturing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 CustomLinks: – Url: https://dx.doi.org/doi:10.1007/s10845-024-02513-0 Name: EDS - Springer Nature Journals (s7799221) Category: fullText Text: View record at Springer |
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| Items | – Name: Title Label: Title Group: Ti Data: Online simulation task scheduling in cloud manufacturing with cross attention and deep reinforcement learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chen%2C+Zhen%22">Chen, Zhen</searchLink><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Lin%22">Zhang, Lin</searchLink><br /><searchLink fieldCode="AR" term="%22Laili%2C+Yuanjun%22">Laili, Yuanjun</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Xiaohan%22">Wang, Xiaohan</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Fei%22">Wang, Fei</searchLink> – Name: TitleSource Label: Source Group: Src Data: Journal of Intelligent Manufacturing; Dec2025, Vol. 36 Issue 8, p5779-5800, 22p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Scheduling%22">Scheduling</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+computer+simulation%22">Digital computer simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Manufacturing+industries%22">Manufacturing industries</searchLink><br /><searchLink fieldCode="DE" term="%22Automated+planning+%26+scheduling%22">Automated planning & scheduling</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Online simulation task scheduling in a private cloud manufacturing platform usually requires rapid decision-making algorithms because of the characteristics of unpredictability and diversity of tasks. However, the existing approaches face challenges in generating satisfactory scheduling schemes within a limited solving time. Therefore, this paper proposes a dynamic scheduling algorithm for online simulation task scheduling that is based on cross-attention and deep reinforcement learning (DRL). A multichannel DRL-based framework with discrete event triggering is introduced to effectively recognize online scheduling environments. An innovative multistep state feature cross-attention method is proposed to address the challenge of temporal features caused by nonsimultaneous task arrivals. A case study in the semiconductor display industry with 35 diverse scheduling scenarios was conducted to evaluate the efficacy of the proposed algorithm, which was compared with six classic state-of-the-art DRL algorithms and three commonly used priority dispatching rules. The results show that the proposed algorithm maintains superior scheduling performance across multiple scheduling scenarios and outperforms the other algorithms by an average of nearly 30% when the optimization objective is considered. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Journal of Intelligent Manufacturing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10845-024-02513-0 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 5779 Subjects: – SubjectFull: Reinforcement learning Type: general – SubjectFull: Scheduling Type: general – SubjectFull: Digital computer simulation Type: general – SubjectFull: Manufacturing industries Type: general – SubjectFull: Automated planning & scheduling Type: general Titles: – TitleFull: Online simulation task scheduling in cloud manufacturing with cross attention and deep reinforcement learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chen, Zhen – PersonEntity: Name: NameFull: Zhang, Lin – PersonEntity: Name: NameFull: Laili, Yuanjun – PersonEntity: Name: NameFull: Wang, Xiaohan – PersonEntity: Name: NameFull: Wang, Fei IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 09565515 Numbering: – Type: volume Value: 36 – Type: issue Value: 8 Titles: – TitleFull: Journal of Intelligent Manufacturing Type: main |
| ResultId | 1 |