改进遗传算法求解柔性job-shop调度问题

本文提出了一种改进遗传算法用于求解柔性作业调度问题(FJSP).针对工序在不同的机器上加工的差异性,我们提出了用能力系数来表征机器的加工能力,不仅可以简化处理而且也较为符合实际情况.该改进算法通过轮换的方法,将加工任务分配到不同的并行机器上去执行,有利于机器的负载平衡.同时,在方法的实现过程中,利用面向对象的思想,将问题进行抽象,用不同的类封装车间,机器和工序信息,这不仅符合现代编程风格,简化编程,也有利于系统的扩展和重构.仿真结果表明,不仅整个加工过程的执行时间得到了优化,而且各类机器完成的操作数相同,使用的时间也较为平均,达到了设计目标.同时该方法的计算速度也较快,适用于较大规模作业车间调度问题的求解.

Modified genetic algorithms solving flexible job-shop scheduling problems

In this paper, modified genetic algorithms for solving flexible job shop scheduling problems (FJSP) are proposed. As the difference of the working procedure on different machines is concerned, we have proposed capacity coefficients, which describe the processing capacity of the machine. We distribute the tasks to different machines by rotation, which benefits for load balance. Considering object oriented, we encapsulate shop, machine and working procedure to different classes, which benefit for system extension and re building. It also simplifies the programming. The simulation results show that not only the whole processing time has been optimized, but also the differences of operations and occupied time on each machine are very little. At the same time the algorithm has a high calculation speed that is fit for large scale scheduling problem.