Parallel machine scheduling optimisation based on an improved multi-objective artificial bee colony algorithm.
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| Title: | Parallel machine scheduling optimisation based on an improved multi-objective artificial bee colony algorithm. |
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| Authors: | Li-Jun Yang1 |
| Source: | International Journal of Information Technology & Management. 2023, Vol. 22 Issue 3/4, p213-225. 13p. |
| Abstract: | Aiming at the scheduling model of the same kind of machine, considering that low carbon emission is an urgent problem to be solved in the manufacturing industry, a mathematical model containing the maximum completion time and maximum processing energy consumption was established. In order to balance the local development ability and global search ability of an artificial bee colony algorithm, and improve the convergence speed of the algorithm, a scheduling optimisation method of parallel machine based on improved multi-objective ABC algorithm was proposed. Firstly, a chaotic image initialisation method is proposed to ensure the diversity and excellence of the initial population. Then, the individual threshold is used to dynamically adjust the search radius to improve the search accuracy and convergence speed. Finally, considering the development times of the external archive solution, the evolution is guided by selecting the elite solution reasonably. In order to verify the effectiveness of the algorithm, comparative experiments and performance analysis of the algorithm are carried out on several examples. The results show that the proposed algorithm can solve the scheduling problem of the same kind of machine effectively in practical scenarios. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Information Technology & Management is the property of Inderscience Enterprises Ltd. 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 |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 172936404 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Parallel machine scheduling optimisation based on an improved multi-objective artificial bee colony algorithm. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Li-Jun+Yang%22">Li-Jun Yang</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Information+Technology+%26+Management%22">International Journal of Information Technology & Management</searchLink>. 2023, Vol. 22 Issue 3/4, p213-225. 13p. – Name: Abstract Label: Abstract Group: Ab Data: Aiming at the scheduling model of the same kind of machine, considering that low carbon emission is an urgent problem to be solved in the manufacturing industry, a mathematical model containing the maximum completion time and maximum processing energy consumption was established. In order to balance the local development ability and global search ability of an artificial bee colony algorithm, and improve the convergence speed of the algorithm, a scheduling optimisation method of parallel machine based on improved multi-objective ABC algorithm was proposed. Firstly, a chaotic image initialisation method is proposed to ensure the diversity and excellence of the initial population. Then, the individual threshold is used to dynamically adjust the search radius to improve the search accuracy and convergence speed. Finally, considering the development times of the external archive solution, the evolution is guided by selecting the elite solution reasonably. In order to verify the effectiveness of the algorithm, comparative experiments and performance analysis of the algorithm are carried out on several examples. The results show that the proposed algorithm can solve the scheduling problem of the same kind of machine effectively in practical scenarios. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Information Technology & Management is the property of Inderscience Enterprises Ltd. 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: 10.1504/IJITM.2023.131807 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 213 Titles: – TitleFull: Parallel machine scheduling optimisation based on an improved multi-objective artificial bee colony algorithm. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Li-Jun Yang IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: 2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 14614111 Numbering: – Type: volume Value: 22 – Type: issue Value: 3/4 Titles: – TitleFull: International Journal of Information Technology & Management Type: main |
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