基于遗传算法的大型Flow—shop生产调度

Flow—shop调度问题具有建模复杂性、计算复杂性、动态多约束、多目标性等特点。近几年，各种演化计算方法逐渐被引入到生产调度中，特别是遗传算法的应用。为此，应用Matlab开发生产调度程序，并利用实际生产数据进行了仿真；通过相关仿真实验，验证了不同交叉算子和变异算子组合获得的最优解存在差异，获得并验证了一种较好的交叉算子和变异算子组合，其仿真调度数据验证了遗传算法用于求解大型流水车间调度的可行性和有效性。

Large-scale flow-shop scheduling based on genetic algorithm

The flow-shop scheduling problem has the property of modeling complexity, computational complexity, dynamic multi-constraint and multi-targeted. In recent years a variety of evolutionary computation methods, in particular, the application of genetic algorithms has been gradually introduced into the production scheduling problem. So, we design a new production scheduler program by Matlab based on the genetic algorithm method, and the actual production data is used to simulation. Moreover, through relevant simulation results we have verified that the differences existed in the optimal solution which from the combination of different crossover operators and mutation operator, and further obtained the better combination of crossover operator and mutation operator. Simulation results of our experiment show the feasibility and effectiveness of genetic algorithm for solving large-scale flow-shop scheduling.