基于遗传算法的柔性车间调度优化

针对传统作业车间调度问题有加工设备功能单一、加工工序对应设备固定的特点，提出了一种新型混合改进的遗传算法进行求解优化。首先，采用一种新的编码思想构建双层染色体编码结构，强化初始种群质量，提高种群的多样性；并给出了相应的选择操作设计，交叉操作采用单段交叉、两段交叉和三段交叉机制，改善算法全局搜索能力，变异操作引入了种群分割的思想，按照适应度将种群分割成两部分，并赋予不同的变异概率，实行两种变异机制，以提高算法的局部搜索能力；添加了新的检查操作以增强优化过程的可行性。最后采用MATLAB编程，通过一个6个工件，每工件6道工序的应用实例进行测试，验证了该方法切实可行，有效。

A Scheduling Optimization about Flexible Job-Shop using Genetic Algorithm

Aiming at the traditional job shop scheduling problem, the processing equipment has a single function and the processing equipment is fixed. A new mixed improved genetic algorithm is proposed to solve the problem. Firstly, a new coding idea is used to construct a two-layer chromosome coding structure, which strengthens the initial population quality and improves the diversity of the population. The corresponding selection operation design is given. The cross operation uses single-segment crossover, double-segment crossover and three-segment crossover. The crossover mechanism improves the global search ability of the algorithm. The mutation operation introduces the idea of population segmentation. The population is divided into two parts according to the fitness, and different mutation probabilities are assigned. Two mutation mechanisms are implemented to improve the local search ability of the algorithm. New inspection operations are added to enhance the feasibility of the optimization process. Finally, using MATLAB programming, through a 6 workpieces, the application examples of 6 processes per workpiece were tested, which verified that the method is feasible and effective.