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Scenario-to-objective transformation for robust cascaded flow-shop joint scheduling: a many-objective optimization framework.

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Title: Scenario-to-objective transformation for robust cascaded flow-shop joint scheduling: a many-objective optimization framework.
Authors: Li, Qiuying1 (AUTHOR) liqiuying@shu.edu.cn, Pan, Quanke1 (AUTHOR) panquanke@shu.edu.cn, Gao, Liang1,2 (AUTHOR) gaoliang@mail.hust.edu.cn, Li, Weimin1,3 (AUTHOR) wmli@shu.edu.cn, Yang, Shengxiang1,4,5 (AUTHOR) syang@dmu.ac.uk
Source: Expert Systems with Applications. Apr2026, Vol. 304, pN.PAG-N.PAG. 1p.
Subjects: Multi-objective optimization, Flow shop scheduling, Constraint satisfaction, Scheduling, Genetic algorithms, Evolutionary algorithms, Mathematical optimization
Abstract: The cascaded flow-shop joint scheduling problem (CFJSP) in printed circuit board manufacturing presents considerable challenges due to processing time uncertainty and inter-phase dependencies. Traditional approaches typically rely on deterministic assumptions or worst-case analysis, which limit their ability to accommodate real-world variability. This study reformulates the uncertain CFJSP as a many-objective optimization problem, treating each scenario-specific makespan as an independent objective. Each scenario represents a distinct realization of processing time variability, introducing implicit complex constraints that must be simultaneously satisfied to ensure robust and feasible scheduling. To address this, a multi-population co-evolutionary greedy algorithm is developed, incorporating fitness-aware offspring generation and constraint-preserving operators to maintain solution feasibility across all scenarios. A knowledge-guided interaction mechanism facilitates inter-population learning, improving convergence and maintaining diversity. Additionally, two greedy-based refinement mechanisms are introduced to intensify the search in promising regions, and a hybrid mutation operator is employed to strategically perturb elite solutions, preventing premature convergence and promoting global exploration. Extensive experiments on benchmark instances show that the proposed method significantly outperforms five state-of-the-art many-objective evolutionary algorithms in terms of convergence, diversity, and scheduling robustness, demonstrating its effectiveness and practical value for solving complex constrained scheduling problems under uncertainty. [ABSTRACT FROM AUTHOR]
Copyright of Expert Systems with Applications is the property of Pergamon Press - An Imprint of Elsevier Science 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: 191296987
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PubTypeId: academicJournal
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  Data: Scenario-to-objective transformation for robust cascaded flow-shop joint scheduling: a many-objective optimization framework.
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  Data: <searchLink fieldCode="AR" term="%22Li%2C+Qiuying%22">Li, Qiuying</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> liqiuying@shu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Pan%2C+Quanke%22">Pan, Quanke</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> panquanke@shu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Gao%2C+Liang%22">Gao, Liang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> gaoliang@mail.hust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Weimin%22">Li, Weimin</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<i> wmli@shu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Yang%2C+Shengxiang%22">Yang, Shengxiang</searchLink><relatesTo>1,4,5</relatesTo> (AUTHOR)<i> syang@dmu.ac.uk</i>
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  Data: <searchLink fieldCode="JN" term="%22Expert+Systems+with+Applications%22">Expert Systems with Applications</searchLink>. Apr2026, Vol. 304, pN.PAG-N.PAG. 1p.
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  Data: <searchLink fieldCode="DE" term="%22Multi-objective+optimization%22">Multi-objective optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Flow+shop+scheduling%22">Flow shop scheduling</searchLink><br /><searchLink fieldCode="DE" term="%22Constraint+satisfaction%22">Constraint satisfaction</searchLink><br /><searchLink fieldCode="DE" term="%22Scheduling%22">Scheduling</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Evolutionary+algorithms%22">Evolutionary algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink>
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  Data: The cascaded flow-shop joint scheduling problem (CFJSP) in printed circuit board manufacturing presents considerable challenges due to processing time uncertainty and inter-phase dependencies. Traditional approaches typically rely on deterministic assumptions or worst-case analysis, which limit their ability to accommodate real-world variability. This study reformulates the uncertain CFJSP as a many-objective optimization problem, treating each scenario-specific makespan as an independent objective. Each scenario represents a distinct realization of processing time variability, introducing implicit complex constraints that must be simultaneously satisfied to ensure robust and feasible scheduling. To address this, a multi-population co-evolutionary greedy algorithm is developed, incorporating fitness-aware offspring generation and constraint-preserving operators to maintain solution feasibility across all scenarios. A knowledge-guided interaction mechanism facilitates inter-population learning, improving convergence and maintaining diversity. Additionally, two greedy-based refinement mechanisms are introduced to intensify the search in promising regions, and a hybrid mutation operator is employed to strategically perturb elite solutions, preventing premature convergence and promoting global exploration. Extensive experiments on benchmark instances show that the proposed method significantly outperforms five state-of-the-art many-objective evolutionary algorithms in terms of convergence, diversity, and scheduling robustness, demonstrating its effectiveness and practical value for solving complex constrained scheduling problems under uncertainty. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Expert Systems with Applications is the property of Pergamon Press - An Imprint of Elsevier Science 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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      – Type: doi
        Value: 10.1016/j.eswa.2025.130725
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      – Code: eng
        Text: English
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        PageCount: 1
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      – SubjectFull: Multi-objective optimization
        Type: general
      – SubjectFull: Flow shop scheduling
        Type: general
      – SubjectFull: Constraint satisfaction
        Type: general
      – SubjectFull: Scheduling
        Type: general
      – SubjectFull: Genetic algorithms
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      – SubjectFull: Evolutionary algorithms
        Type: general
      – SubjectFull: Mathematical optimization
        Type: general
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      – TitleFull: Scenario-to-objective transformation for robust cascaded flow-shop joint scheduling: a many-objective optimization framework.
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            NameFull: Li, Qiuying
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            NameFull: Pan, Quanke
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            NameFull: Gao, Liang
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            NameFull: Li, Weimin
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            – D: 01
              M: 04
              Text: Apr2026
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              Y: 2026
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