Abstract This paper integrates energy awareness in the flexible flow shop scheduling system, where two objectives are minimized simultaneously: total tardiness and electric power costs. We also consider practical settings including variable processing speeds and time-of-use (TOU) electricity prices. A novel hybrid particle swarm optimization (HPSO) algorithm is developed which incorporates several distinguishing features: Particles are represented based on job operation and machine assignment, which are updated directly in the discrete domain. More importantly, we introduce a multi-objective tabu search procedure and a position based crossover operator to balance global exploration and local exploitation. Experiments are conducted to verify the performance of the proposed HPSO algorithm compared to the well-known approaches in the literature. Results show the significance of HPSO in terms of the number and quality of non-dominated solutions and computational efficiency.

中文翻译：

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具有混合粒子群优化的柔性流水车间的能量感知调度

摘要 本文在灵活的流水车间调度系统中集成了能源意识，其中两个目标同时最小化：总延误和电力成本。我们还考虑了实际设置，包括可变处理速度和使用时间 (TOU) 电价。开发了一种新颖的混合粒子群优化 (HPSO) 算法，该算法包含几个显着特征： 基于作业操作和机器分配表示粒子，这些粒子直接在离散域中更新。更重要的是，我们引入了多目标禁忌搜索程序和基于位置的交叉算子来平衡全局探索和局部开发。与文献中众所周知的方法相比，进行了实验以验证所提出的 HPSO 算法的性能。