What does this research mean for the field?

The proposed Adaptive Co-Evolutionary Memetic Algorithm (ACMA) effectively minimizes makespan in hybrid flow shop scheduling problems with sequence-dependent setup and transportation times, matching exact optimal solutions on small-scale instances and achieving competitive performance on large-scale instances. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To formulate an exact mathematical model and design an adaptive co-evolutionary metaheuristic that minimizes makespan in hybrid flow shop scheduling with unrelated parallel machines, setup times, and transportation constraints.

August 29, 2026MachinesOpen Access

An Adaptive Co-Evolutionary Memetic Algorithm for a Hybrid Flow Shop Scheduling Problem with Sequence-Dependent Setup and Transportation Times

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To formulate an exact mathematical model and design an adaptive co-evolutionary metaheuristic that minimizes makespan in hybrid flow shop scheduling with unrelated parallel machines, setup times, and transportation constraints.
Formulated a machine-position-based mixed-integer linear programming (MILP) model to determine exact optimal schedules on small-scale instances.
Engineered an adaptive co-evolutionary memetic algorithm (ACMA) pairing a genetic algorithm for exploration with a Lévy flight-enhanced particle swarm optimization for local search via a dynamic role allocation mechanism.
Incorporated a progressive two-stage memetic enhancement using deep variable neighborhood search and catastrophe-based diversification, validating performance against benchmark algorithms via Wilcoxon signed-rank tests.
ACMA matched the exact optimal makespan solutions derived from the MILP model across all evaluated small-scale instances.
The algorithm demonstrated competitive performance improvements over standard benchmark algorithms on large-scale, complex problem instances.
Wilcoxon signed-rank tests confirmed that performance differences between ACMA and comparative benchmark algorithms were statistically significant.

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Implication

Optimization study demonstrates superior makespan minimization across complex hybrid flow shop benchmarks, indicating effective coordination of global search and local exploitation.

Cite This Study

Wang et al. (2026) studied this question.

synapsesocial.com/papers/6a9299ac8e5d7d1fc0c11b81 https://doi.org/10.3390/machines14090969

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Implication

Optimization study demonstrates superior makespan minimization across complex hybrid flow shop benchmarks, indicating effective coordination of global search and local exploitation.

An Adaptive Co-Evolutionary Memetic Algorithm for a Hybrid Flow Shop Scheduling Problem with Sequence-Dependent Setup and Transportation Times

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Discussion

Implication

Cite This Study

An Adaptive Co-Evolutionary Memetic Algorithm for a Hybrid Flow Shop Scheduling Problem with Sequence-Dependent Setup and Transportation Times

Key Points

Discussion

Implication

Cite This Study

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