In this study, an energy-efficient unrelated parallel machine scheduling problem is discussed. The speed scaling mechanism has been taken into account as an energy-efficient strategy. Unrelated parallel machine scheduling with speed scaling is generalized by considering machine-sequence dependent setup times and learning effect features. A multiobjective mixed-integer linear programming (MILP) model has been proposed for the problem. Due to the NP-hard nature of the problem, a multiobjective evolutionary algorithm, the NSGA-II-based memetic algorithm, is proposed. An encoding scheme, decoding algorithm, and local search algorithms are proposed for the problem. Speed tuning heuristic and job-machine switch heuristic algorithms are proposed as local search algorithms. A restarting strategy has been applied to ensure the diversification of the algorithm. The classical NSGA-II algorithm and the proposed memetic algorithm were compared over the generated test problems. As a result, the proposed memetic algorithm is more successful according to performance metrics.

在这项研究中，讨论了一个节能无关的并行机调度问题。速度缩放机制已被视为一种节能策略。通过考虑与机器序列相关的设置时间和学习效果特征，可以概括具有速度缩放的无关并行机器调度。针对该问题提出了多目标混合整数线性规划 (MILP) 模型。由于该问题的NP难性质，提出了一种多目标进化算法，即基于NSGA-II的模因算法。针对该问题提出了编码方案、解码算法和局部搜索算法。提出了速度调整启发式和作业-机器切换启发式算法作为局部搜索算法。已应用重新启动策略以确保算法的多样化。在生成的测试问题上比较了经典的 NSGA-II 算法和提出的模因算法。因此，根据性能指标，所提出的模因算法更成功。