改进遗传算法求解柔性作业车间调度问题

分析柔性作业车间调度问题的特点，提出一种求解该问题的改进遗传算法。在考虑各个机器负荷平衡，所有机器上的总负荷和最大完工时间等性能指标更加合理情况下，设计一种全局搜索、局部搜索和随机产生相结合的初始化方法，提高种群初始解的质量，加快遗传算法的收敛速度。结合问题特点设计合理的染色体编码方式、交叉算子和变异算子，防止遗传操作过程中非法解的产生，避免染色体的修复，提高求解效率。使用文献中相同的实例测试利用初始化方法的改进遗传算法，并将计算结果与文献中其他遗传算法的测试结果进行比较，验证所提出的初始化方法的可行性和有效性。

Improved Genetic Algorithm for the Flexible Job-shop Scheduling Problem

The characteristic of the flexible job shop scheduling problem (FJSP) is analyzed, an improved genetic algorithm is proposed to solve the FJSP. To keep workload balance among the machines, improve the quality of the initial population and accelerate the speed of the algorithm's convergence, a new initialization method is proposed, which combines with global search, local search and random generation. Considering the characteristic of the problem, rational chromosome encoding, crossover operator and mutation operator are designed to prevent the generation of illegal solutions, avoid chromosome repair and improve efficiency of the algorithm. The improved genetic algorithm is tested on instances taken from the literature and compared with their results. The computation results show that the improved genetic algorithm is feasible and effective.