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A Two-Stage Cooperative Evolutionary Algorithm With Problem-Specific Knowledge for Energy-Efficient Scheduling of

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    This study introduces a new algorithm for energy-efficient scheduling in manufacturing, focusing on reducing makespan and energy use in no-wait flow-shop problems.

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    Area of Science:

    • Manufacturing Operations Research
    • Computational Intelligence
    • Sustainable Manufacturing

    Background:

    • Green scheduling and energy saving are critical in manufacturing.
    • No-wait flow-shop scheduling is widely applied but often overlooks energy consumption.
    • Optimizing both production time and energy use is a significant challenge.

    Purpose of the Study:

    • To develop an energy-efficient scheduling method for the no-wait flow-shop problem.
    • To minimize both the makespan and total energy consumption.
    • To introduce a novel two-stage cooperative evolutionary algorithm (TS-CEA).

    Main Methods:

    • Proposed a two-stage cooperative evolutionary algorithm (TS-CEA) incorporating problem-specific knowledge.
    • Designed two constructive heuristics for initial solution generation.
    • Employed iterative local search (ILS) and a hybrid neighborhood structure in the first stage.
    • Utilized a critical path-based mutation strategy and a co-evolutionary system in the second stage.

    Main Results:

    • TS-CEA effectively addresses the energy-efficient no-wait flow-shop problem (EENWFSP).
    • The algorithm demonstrates strong performance in minimizing makespan and total energy consumption.
    • Numerical results validate the effectiveness and efficiency of the proposed TS-CEA.

    Conclusions:

    • TS-CEA provides a robust solution for energy-efficient scheduling in no-wait flow-shop environments.
    • The integration of problem-specific knowledge enhances scheduling optimization.
    • The study contributes to sustainable practices in manufacturing scheduling.