The permutation flowshop scheduling problem (PFSP) has been extensively explored in scheduling literature because it has many real-world industrial implementations. In some studies, multiple objectives related to production efficiency have been considered simultaneously. However, studies that consider energy consumption and environmental impacts are very rare in a multi-objective setting. In this work, we studied two contradictory objectives, namely, total flowtime and total energy consumption (TEC) in a green permutation flowshop environment, in which the machines can be operated at varying speed levels corresponding to different energy consumption values. A bi-objective mixed-integer programming model formulation was developed for the problem using a speed-scaling framework. To address the conflicting objectives of minimizing TEC and total flowtime, the augmented epsilon-constraint approach was employed to obtain Pareto-optimal solutions. We obtained near approximations for the Pareto-optimal frontiers of small-scale problems using a very small epsilon level. Furthermore, the mathematical model was run with a time limit to find sets of non-dominated solutions for large instances. As the problem was NP-hard, two effective multi-objective iterated greedy algorithms and a multi-objective variable block insertion heuristic were also proposed for the problem as well as a novel construction heuristic for initial solution generation. The performance of the developed heuristic algorithms was assessed on well-known benchmark problems in terms of various quality measures. Initially, the performance of the algorithms was evaluated on small-scale instances using Pareto-optimal solutions. Then, it was shown that the developed algorithms are tremendously effective for solving large instances in comparison to time-limited model.

置换流水车间调度问题（PFSP）已在调度文献中进行了广泛研究，因为它具有许多实际的工业实现方式。在某些研究中，同时考虑了与生产效率相关的多个目标。但是，在多目标环境中很少考虑能耗和环境影响的研究。在这项工作中，我们研究了两个相互矛盾的目标，即在绿色排列流水车间环境中的总流动时间和总能耗（TEC），在这些环境中，机器可以以与不同能耗值相对应的不同速度水平运行。使用速度缩放框架针对该问题开发了双目标混合整数编程模型。为了解决使TEC和总流动时间最小化的矛盾目标，采用了增强的epsilon约束方法来获得帕累托最优解。我们使用非常小的ε水平获得了小规模问题的帕累托最优边界的近似值。此外，运行数学模型时有时间限制，可以找到大型实例的非支配解集。由于问题是NP难的，因此还针对该问题提出了两种有效的多目标迭代贪婪算法和多目标变量块插入启发式算法，以及用于初始解生成的新颖构造启发式算法。在各种质量度量方面，对众所周知的基准问题评估了开发的启发式算法的性能。原来，使用帕累托最优解在小规模实例上评估了算法的性能。然后，表明与时限模型相比，所开发的算法对于求解大型实例非常有效。