Large manufacturing companies often manage a network of multiple factories, creating a distributed flow shop scheduling problem for flowline manufacturing processes. This problem involves assigning jobs to one of several distributed factories, each equipped with identical flow shops, and completing the jobs within their designated factory. We expand upon the traditional distributed flow shop scheduling problem by incorporating the transportation of intermediate goods between factories. This transportation can occur after each machine, with shipping times dependent on the distance between the origin and destination factories, potentially delaying further processing. Our objective is to minimize the makespan across all factories. We introduce an iterated greedy search procedure specifically designed for distributed flow shop scheduling with inter-factory transportation. Based on a graph representation, a speed-up procedure is developed to improve the algorithms search ability. Through computational studies, we demonstrate the effectiveness and efficiency of our proposed algorithm, and provide insights into the benefits of transportation. Our findings indicate that incorporating transportation enhances machine utilization across factories by alleviating bottlenecks, ultimately improving overall efficiency and reducing makespan. Besides, we are able to quantify the trade-off between transport times and the benefits of inter-factory transportation.

中文翻译：

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工厂间运输的分布式流水车间调度问题


大型制造公司通常管理着一个由多个工厂组成的网络，这给流水线制造流程带来了分布式流水车间调度问题。此问题涉及将工作分配给多个分布式工厂之一，每个工厂都配备了相同的流水车间，并在其指定的工厂内完成工作。我们通过整合工厂之间的中间货物运输来扩展传统的分布式流车间调度问题。这种运输可能在每台机器之后进行，运输时间取决于原产地和目的地工厂之间的距离，可能会延迟进一步的加工。我们的目标是最大限度地减少所有工厂的制造时间。我们引入了一个迭代的贪婪搜索程序，该程序专为具有工厂间运输的分布式流水车间调度而设计。基于图表示，开发了一种加速程序来提高算法的搜索能力。通过计算研究，我们证明了我们提出的算法的有效性和效率，并提供了对交通优势的见解。我们的研究结果表明，整合运输通过缓解瓶颈来提高工厂的机器利用率，最终提高整体效率并缩短制造时间。此外，我们能够量化运输时间和工厂间运输的好处之间的权衡。