基于遗传算法改进的动态车间调度

Dynamic plant scheduling based on the improved genetic algorithm

由于经典的车间调度方法大都将生产系统中各种加工参数看作确定性的精确值,且将生产系统当作一个静态的系统,忽略了实际加工过程中的各种突发状况.因此从动态生产环境下生产运作和管理的实际需求出发,考虑到生产过程中的加工参数非确定性精确值、动态扰动等因素的影响,基于改进遗传算法开展了动态生产环境下的作业车间调度问题的研究工作.将模糊化参数引入调度模型,通过改进来避免一般遗传算法收敛过快的问题,从而寻求到动态车间调度的最优解.

The classical plant scheduling always took various processing parameters of the production systems as the exact certainty value, and took production system as a static system where a variety of unexpected situations of the actual processing is ignored. So the actual demand of production operation and management of dynamic production environment, and nondeterministic exact value, disturbance and other factors of the processing parameters in the production process are taken into account in this paper. The plant scheduling research question of a dynamic production environment based on improved genetic algorithm is carried out. In order to avoid getting convergence too fast of the genetic algorithm and the optimization from dynamic plant scheduling can be found, the approach of putting the fuzzy theory into the genetic algorithm is used.