Academic Journal
Learning-Driven Portfolio–Island Decoding for Adaptive Scheduling Problems.
| Τίτλος: | Learning-Driven Portfolio–Island Decoding for Adaptive Scheduling Problems. |
|---|---|
| Συγγραφείς: | El-Amine Meziane, Mohammed1 (AUTHOR) amine.meziane@ese-oran.dz |
| Πηγή: | International Journal on Artificial Intelligence Tools. May2026, Vol. 35 Issue 3, p1-34. 34p. |
| Θεματικοί όροι: | *Scheduling, *Flexible manufacturing systems, Decoding algorithms, Multi-armed bandit problem (Probability theory), Mobile robots, Evolutionary algorithms |
| Περίληψη: | Integrated production–transport scheduling with mobile robots tightly couples manufacturing operations and internal logistics, resulting in complex decision interactions that challenge conventional optimization approaches. Many existing evolutionary frameworks rely on a single decoding strategy throughout the search, limiting their ability to adapt to heterogeneous instance structures and dynamic scheduling characteristics. This paper introduces a learning-driven portfolio–island framework that shifts the focus from designing new operators to learning which decoding strategies are most effective during the search process. Multiple heterogeneous decoders evolve in parallel on separate islands, while an upper confidence bound multi-armed bandit continuously evaluates their contributions to makespan improvement and dynamically reallocates population resources to balance exploration and exploitation. The proposed framework is evaluated under different scheduling settings, including pure makespan minimization and extended production–transport coordination scenarios. Computational experiments on benchmark datasets demonstrate that the portfolio–learning mechanism consistently improves solution quality and convergence stability compared with single-decoder approaches, particularly for large and structurally diverse instances. These results highlight adaptive decoder selection as a powerful mechanism for robust and scalable scheduling in mobile-robot-supported manufacturing systems, providing a foundation for intelligent decision-making in production environments. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal on Artificial Intelligence Tools is the property of World Scientific Publishing Company 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.) | |
| Βάση Δεδομένων: | Business Source Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: bsx DbLabel: Business Source Index An: 194725829 RelevancyScore: 1412 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1411.61413574219 |
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| Items | – Name: Title Label: Title Group: Ti Data: Learning-Driven Portfolio–Island Decoding for Adaptive Scheduling Problems. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22El-Amine+Meziane%2C+Mohammed%22">El-Amine Meziane, Mohammed</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> amine.meziane@ese-oran.dz</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+on+Artificial+Intelligence+Tools%22">International Journal on Artificial Intelligence Tools</searchLink>. May2026, Vol. 35 Issue 3, p1-34. 34p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Scheduling%22">Scheduling</searchLink><br />*<searchLink fieldCode="DE" term="%22Flexible+manufacturing+systems%22">Flexible manufacturing systems</searchLink><br /><searchLink fieldCode="DE" term="%22Decoding+algorithms%22">Decoding algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Multi-armed+bandit+problem+%28Probability+theory%29%22">Multi-armed bandit problem (Probability theory)</searchLink><br /><searchLink fieldCode="DE" term="%22Mobile+robots%22">Mobile robots</searchLink><br /><searchLink fieldCode="DE" term="%22Evolutionary+algorithms%22">Evolutionary algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Integrated production–transport scheduling with mobile robots tightly couples manufacturing operations and internal logistics, resulting in complex decision interactions that challenge conventional optimization approaches. Many existing evolutionary frameworks rely on a single decoding strategy throughout the search, limiting their ability to adapt to heterogeneous instance structures and dynamic scheduling characteristics. This paper introduces a learning-driven portfolio–island framework that shifts the focus from designing new operators to learning which decoding strategies are most effective during the search process. Multiple heterogeneous decoders evolve in parallel on separate islands, while an upper confidence bound multi-armed bandit continuously evaluates their contributions to makespan improvement and dynamically reallocates population resources to balance exploration and exploitation. The proposed framework is evaluated under different scheduling settings, including pure makespan minimization and extended production–transport coordination scenarios. Computational experiments on benchmark datasets demonstrate that the portfolio–learning mechanism consistently improves solution quality and convergence stability compared with single-decoder approaches, particularly for large and structurally diverse instances. These results highlight adaptive decoder selection as a powerful mechanism for robust and scalable scheduling in mobile-robot-supported manufacturing systems, providing a foundation for intelligent decision-making in production environments. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal on Artificial Intelligence Tools is the property of World Scientific Publishing Company 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: 10.1142/S021821302650003X Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 34 StartPage: 1 Subjects: – SubjectFull: Scheduling Type: general – SubjectFull: Flexible manufacturing systems Type: general – SubjectFull: Decoding algorithms Type: general – SubjectFull: Multi-armed bandit problem (Probability theory) Type: general – SubjectFull: Mobile robots Type: general – SubjectFull: Evolutionary algorithms Type: general Titles: – TitleFull: Learning-Driven Portfolio–Island Decoding for Adaptive Scheduling Problems. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: El-Amine Meziane, Mohammed IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 02182130 Numbering: – Type: volume Value: 35 – Type: issue Value: 3 Titles: – TitleFull: International Journal on Artificial Intelligence Tools Type: main |
| ResultId | 1 |