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A hybrid approach for the dynamic flexible job shop scheduling problem considering machine failures.

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Title: A hybrid approach for the dynamic flexible job shop scheduling problem considering machine failures.
Authors: Peng, Chong1 (AUTHOR) pch@buaa.edu.cn, Zhang, Zhongwen1,2 (AUTHOR) zw@buaa.edu.cn, Liao, T. Warren3,4 (AUTHOR) ieliao@lsu.edu, Zhao, Hui1 (AUTHOR) 1183025150@qq.com, Cai, Yuzhen1 (AUTHOR) caiyuzhen@buaa.edu.cn
Source: Journal of Scheduling. Aug2025, Vol. 28 Issue 4, p407-424. 18p.
Subjects: Scheduling, Machine part failures, Artificial intelligence, Genetic algorithms, Industrial efficiency
Abstract: In practical production scheduling, dynamic disturbances such as machine failures frequently disrupt initial schedules. In this research, a new approach using a genetic algorithm prescheduling and machining path routing strategy is proposed to solve the dynamic flexible job shop scheduling problem. Firstly, the efficiency of the scheduling algorithm is improved by a genetic algorithm with an improved active decoding method and a rescheduling algorithm with a dual strategy of right shift and processing path rerouting. Then, a more reasonable solution is obtained by path rerouting in the framework of a prescheduling strategy using a binary tree-based identification system to determine the set of affected processes to reduce the restriction on alternative paths while increasing the search range. Finally, the proposed rescheduling algorithm is compared with two methods through experimental comparisons, which confirms that the algorithm can obtain a more robust and stable solution. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Scheduling 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: Engineering Source
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DbLabel: Engineering Source
An: 187279213
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: A hybrid approach for the dynamic flexible job shop scheduling problem considering machine failures.
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  Data: <searchLink fieldCode="AR" term="%22Peng%2C+Chong%22">Peng, Chong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> pch@buaa.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Zhongwen%22">Zhang, Zhongwen</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> zw@buaa.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Liao%2C+T%2E+Warren%22">Liao, T. Warren</searchLink><relatesTo>3,4</relatesTo> (AUTHOR)<i> ieliao@lsu.edu</i><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Hui%22">Zhao, Hui</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 1183025150@qq.com</i><br /><searchLink fieldCode="AR" term="%22Cai%2C+Yuzhen%22">Cai, Yuzhen</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> caiyuzhen@buaa.edu.cn</i>
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  Data: <searchLink fieldCode="DE" term="%22Scheduling%22">Scheduling</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+part+failures%22">Machine part failures</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Industrial+efficiency%22">Industrial efficiency</searchLink>
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  Label: Abstract
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  Data: In practical production scheduling, dynamic disturbances such as machine failures frequently disrupt initial schedules. In this research, a new approach using a genetic algorithm prescheduling and machining path routing strategy is proposed to solve the dynamic flexible job shop scheduling problem. Firstly, the efficiency of the scheduling algorithm is improved by a genetic algorithm with an improved active decoding method and a rescheduling algorithm with a dual strategy of right shift and processing path rerouting. Then, a more reasonable solution is obtained by path rerouting in the framework of a prescheduling strategy using a binary tree-based identification system to determine the set of affected processes to reduce the restriction on alternative paths while increasing the search range. Finally, the proposed rescheduling algorithm is compared with two methods through experimental comparisons, which confirms that the algorithm can obtain a more robust and stable solution. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Scheduling 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.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=187279213
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        Value: 10.1007/s10951-025-00839-y
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      – Code: eng
        Text: English
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        PageCount: 18
        StartPage: 407
    Subjects:
      – SubjectFull: Scheduling
        Type: general
      – SubjectFull: Machine part failures
        Type: general
      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Genetic algorithms
        Type: general
      – SubjectFull: Industrial efficiency
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      – TitleFull: A hybrid approach for the dynamic flexible job shop scheduling problem considering machine failures.
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            NameFull: Zhang, Zhongwen
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            NameFull: Liao, T. Warren
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            NameFull: Zhao, Hui
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            – D: 01
              M: 08
              Text: Aug2025
              Type: published
              Y: 2025
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