The distributed permutation flowshop scheduling problem (DPFSP) has attracted a lot of attention in recent years, as current production systems frequently operate multiple factories at once. Due to the practical relevance of mixed no-idle flowshop, we address an extension of the DPFSP where there are identical factories, each one with a mixed no-idle flowshop environment. In a mixed no-idle flowshop regular machines exist side-by-side with stages that require a non-stop process. The addressed problem also takes into account sequence-dependent setup times on conventional machines. To the extent of our knowledge, this issue has not yet been examined in the literature despite it can be encountered in current manufacturing systems. For the purpose, we present a MILP formulation, a novel constructive heuristic and iterated greedy algorithms. A new construction scheme based on inter-factory and intra-factory improvement schemes is provided for the novel heuristic to further improve the solution quality given by the method. For the iterated greedy algorithm, we developed a new efficient reconstruction strategy to enhance the exploration capability of the algorithm. Furthermore, a large new benchmark was generated using a well recognized set of instances. The state-of-the-art methods and our proposed algorithms were tested in an extensive statistical and computational experiment. The analysis show that our proposals outperformed the methods from the literature.

近年来，由于当前的生产系统经常同时运行多个工厂，因此分布式排列流水车间调度问题（DPFSP）引起了很多关注。由于混合无怠速流水车间的实际意义，我们解决了DPFSP的扩展问题，在DPFSP中，存在相同的工厂，每个工厂都有混合无怠速流水车间环境。在混合无怠工的流水车间中，常规机器与需要不间断过程的各个阶段并排存在。解决的问题还考虑了传统机器上与序列有关的设置时间。就我们所知，尽管在当前的制造系统中可能遇到此问题，但文献中尚未对此问题进行过研究。为此，我们提出了一种MILP公式，一种新颖的构造启发式和迭代贪婪算法。针对新颖的启发式方法，提供了一种基于工厂间和工厂内改进方案的新构造方案，以进一步提高该方法给出的解决方案质量。对于迭代贪婪算法，我们开发了一种新的有效重构策略来增强算法的探索能力。此外，使用一组公认的实例生成了一个新的大型基准。最新的方法和我们提出的算法已在广泛的统计和计算实验中进行了测试。分析表明，我们的建议优于文献中的方法。我们开发了一种新的高效重建策略，以增强算法的探索能力。此外，使用一组公认的实例生成了一个新的大型基准。最新的方法和我们提出的算法已在广泛的统计和计算实验中进行了测试。分析表明，我们的建议优于文献中的方法。我们开发了一种新的高效重建策略，以增强算法的探索能力。此外，使用一组公认的实例生成了一个新的大型基准。最新的方法和我们提出的算法已在广泛的统计和计算实验中进行了测试。分析表明，我们的建议优于文献中的方法。