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An efficient memetic algorithm for distributed flexible job shop scheduling problem with transfers

机译：一种有效的传输分布式柔性作业商店调度问题的高效遗料

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The traditional distributed flexible job shop scheduling problem (DFJSP) assumes that operations of a job cannot be transferred between different factories. However, in real-world production settings, the operations of a job may need to be processed in different factories owing to requirements of economic globalization or complexity of the job. Hence, in this paper, we propose a distributed flexible job shop scheduling problem with transfers (DFJSPT), in which operations of a job can be processed in different factories. An efficient memetic algorithm (EMA) is proposed to solve the DFJSPT with the objectives of minimizing the makespan, maximum workload, and total energy consumption of factories. In the proposed EMA, a well-designed chromosome presentation and initialization methods are presented to obtain a high-quality initial population. Several crossover and mutation operators and three effective neighborhood structures are designed to expand the search space and accelerate the convergence speed of the solution. Forty benchmark instances of the DFJSPT are constructed to evaluate the EMA and facilitate further studies. The Taguchi method of design of experiments is used to obtain the best combination of key EMA parameters. Extensive computational experiments are carried out to compare the EMA with three well-known algorithms from the literature. The computational results show that the EMA can obtain better solutions for approximately 90% of the tested benchmark instances compared to the three wellknown algorithms, thereby demonstrating the DFJSPT's competitive performance and efficiency. (c) 2020 Elsevier Ltd. All rights reserved.
机译：传统的分布式灵活作业商店调度问题（DFJSP）假定作业的操作不能在不同的工厂之间传输。然而，在现实世界的生产设置中，由于工作的经济全球化或复杂性的要求，可能需要在不同的工厂中处理工作的运营。因此，在本文中，我们提出了一个分布式灵活的作业商店调度问题，其中传输（DFJSPT），其中作业的操作可以在不同的工厂中处理。提出了一种有效的迭代算法（EMA），以解决DFJSPT，目的是最小化MAPESPAN，最大工作量和工厂总能耗的目标。在拟议的EMA中，提出了一种精心设计的染色体呈现和初始化方法以获得高质量的初始群体。若干交叉和突变算子和三个有效的邻域结构旨在扩展搜索空间并加速解决方案的收敛速度。 DFJSPT的四十个基准实例被构建为评估EMA并促进进一步的研究。实验设计的TAGUCHI方法用于获得最佳的关键EMA参数的最佳组合。进行了广泛的计算实验，以将EMA与文献中的三种众所周知的算法进行比较。计算结果表明，与三个众所周知的算法相比，EMA可以获得大约90％的测试基准实例的更好的解决方案，从而展示了DFJSPT的竞争性能和效率。 （c）2020 elestvier有限公司保留所有权利。

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