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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]
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Database: Engineering Source
Description
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]
ISSN:09574174
DOI:10.1016/j.eswa.2025.130725