基于自适应遗传算法的混合流水车间调度研究

混合流水车间调度问题具有建模复杂性、动态多约束、计算复杂性、多目标性等特点，近些年，各种演进化算法被运用到生产调度中，特别是遗传算法的应用。本文提出了自适应遗传算法求解混合Flow-shop调度问题的方法，应用Matlab对算法进行编码，并利用实际的生产数据进行了仿真。通过仿真实验，仿真调度数据验证了自适应遗传算法用于求解大型流水车间调度的可行性和有效性。

Research on the Hybrid Flow Shop Scheduling Based on the Self-Adaptive Genetic Algorithm

The hybrid flow shop scheduling problem has modeling complexity, dynamic multi-constraint, computational complexity, multi-target, among many other characters. In recent years, a variety of evolutionary computation methods, in particular, genetic algorithms have been gradually introduced into production and its scheduling. This paper puts forward a new coding method for the hybrid flow-shop scheduling problem, with Mat-lab algorithm to encode, and uses data from actual production to simulate. According to the results, this method is feasible and effective for large-scale flow shop scheduling.