The flexible job shop scheduling problem is a very important problem in factory scheduling. Most of existing researches only consider the processing time of each operation, however, jobs often require transporting to another machine for the next operation while machines often require setup to process the next job. In addition, the times associated with these steps increase the complexity of this problem. In this paper, the flexible job scheduling problem is solved that incorporates not only processing time but setup time and transportation time as well. After presenting the problem, an improved genetic algorithm is proposed to solve the problem, with the aim of minimizing the makespan time, minimizing total setup time, and minimizing total transportation time. In the improved genetic algorithm, initial solutions are generated through three different methods to improve the quality and diversity of the initial population. Then, a crossover method with artificial pairing is adopted to preserve good solutions and improve poor solutions effectively. In addition, an adaptive weight mechanism is applied to alter mutation probability and search ranges dynamically for individuals in the population. By a series of experiments with standard datasets, we demonstrate the validity of our approach and its strong performance.

灵活的车间调度问题是工厂调度中非常重要的问题。现有的大多数研究都只考虑每个操作的处理时间，但是，作业通常需要转移到另一台机器以进行下一个操作，而机器通常需要设置才能处理下一个作业。另外，与这些步骤相关的时间增加了该问题的复杂性。本文解决了灵活的作业调度问题，该问题不仅包括处理时间，还包括准备时间和运输时间。在提出问题之后，提出了一种改进的遗传算法来解决该问题，其目的是最小化制造时间，最小化总设置时间，以及最小化总运输时间。在改进的遗传算法中 初始解决方案是通过三种不同的方法生成的，以提高初始群体的质量和多样性。然后，采用人工配对的交叉方法来保留好的解并有效地改善差的解。另外，自适应权重机制被应用于动态地改变种群中个体的突变概率和搜索范围。通过使用标准数据集进行的一系列实验，我们证明了该方法的有效性及其强大的性能。应用自适应权重机制来动态更改种群中个体的突变概率和搜索范围。通过使用标准数据集进行的一系列实验，我们证明了该方法的有效性及其强大的性能。应用自适应权重机制来动态更改种群中个体的突变概率和搜索范围。通过使用标准数据集进行的一系列实验，我们证明了该方法的有效性及其强大的性能。