改进遗传算法求解带不相关并行机的HFSP

以最小化最大完工时间为目标的不相关并行机混合流水车间调度问题。首先建立了不相关并行机混合流水车间调度问题的数学模型；然后提出了改进的遗传算法进行求解。为弥补遗传算法的迭代后期容易陷入局部搜索的缺陷，在传统遗传算法的基础上利用改进的自适应交叉和变异概率因子及模拟退火局部搜索策略，增强遗传算法在迭代后期跳出局部最优的能力。并通过两个案例来验证改进遗传算法的有效性。

IMPROVED GENETIC ALGORITHM TO SOLVE HFSP WITH UNRELATED PARALLEL MACHINE

The unrelated parallel machine hybrid flow shop scheduling problem with the goal of minimizing the maximum completion time was studied. Firstly, a mathematical model of uncorrelated parallel machine hybrid flow shop scheduling problem was established; then an improved genetic algorithm was proposed to solve it. In order to make up for the shortcomings of genetic algorithm that was easy to fall into local search in the later iteration, on the basis of the traditional genetic algorithm, the adaptive crossover and mutation probability factors and the simulated annealing local search strategy were used to enhance the genetic algorithm''s ability to jump out of the local optimum at the later stage of the iteration. Two cases were used to verify the effectiveness of the improved genetic algorithm.