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    基于强化学习的正弦优化算法求解能耗分布式流水车间节能调度问题

    A Reinforcement Learning Based Sine Optimization Algorithm for Energy-efficient Distributed Flow Shop Scheduling Problem

    • 摘要: 针对分布式流水车间节能调度中最大完工时间(makespan)与总能耗(TEC)的多目标优化难题,本文提出一种基于强化学习的正弦优化算法(RLSOA)。算法通过双重Q-learning策略协同优化加工序列与速度调整:底层Q-learning优先加速关键路径任务以缩短makespan,顶层Q-learning降低非关键任务速度以减少TEC。结合自适应参数与四种速度调整算子,设计基于精英解导向的局部搜索策略,平衡全局探索与局部开发。基于480组不同规模算例的实验表明,RLSOA在覆盖率(C-metric)和反世代距离(IGD)指标上显著优于对比算法(KCA和INSGA),平均提升23.6%和降低41.8%。消融实验验证双重Q-learning与局部搜索分别贡献65.3%和28.7%的解质量提升。统计检验(p< 0.05)证实算法优越性,为分布式制造系统提供了高效的节能调度工具。

       

      Abstract: To address the multi-objective optimization problem of minimizing makespan and total energy consumption (TEC) in energy-efficient distributed permutation flow shop scheduling (EEDPFSP), this paper proposes a reinforcement learning-based sine optimization algorithm (RLSOA). The algorithm integrates a dual Q-learning strategy: the bottom-level Q-learning prioritizes accelerating critical tasks to reduce makespan, while the top-level Q-learning adjusts processing speeds of non-critical tasks to lower TEC. An adaptive parameter derived from the sine formula dynamically controls the search scope, and a local search strategy guided by elite solutions balances global exploration and local exploitation. Experiments on 480 instances with varying scales (jobs: 20-80, machines: 4-16, factories: 2-5) demonstrate that RLSOA outperforms state-of-the-art algorithms (KCA and INSGA) with 23.6% higher C-metric and 41.8% lower IGD on average. Ablation studies reveal that the dual Q-learning and local search contribute 65.3% and 28.7% to solution quality improvement, respectively. Statistical tests (Friedman and Wilcoxon, p< 0.05) confirm the superiority of RLSOA. The proposed algorithm provides an efficient tool for energy-efficient scheduling in distributed manufacturing systems.

       

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