融合Powell搜索法的遗传算法求解柔性车间调度问题

Solving the problem of flexible job-shop scheduling with genetic algorithm based on Powell searching

针对柔性车间调度问题,提出一种融合Powell搜索法的(改进)遗传算法(GA-Powell)。该算法分别以最大完工时间、机器总载荷和最大机器载荷为优化目标。考虑到柔性车间调度遗传算法中染色体编码方案的特殊性,对传统的Powell搜索法进行改进,以免在进化过程中产生不可行解。利用文献中的测试实例和Brandimarte测试实例测试GAPowell,验证了GA-Powell的可行性和有效性。

Aiming at the problem of flexible job-shop scheduling, an improved Genetic Algorithm based on Powell searching ( GAPowell) is proposed. The optimization targets of the algorithm are the maximum completion time, the total machine load and the maximum machine load respectively. Considering the particularity of the chromosome coding scheme of genetic algorithm aiming at the flexible shop scheduling, the traditional Powell searching algorithm is improved to avoid the generation of infeasible solutions in the evolutionary process. The GA-Powell was tested with the testing cases of the literature and the testing cases of Brandimarte, which proved the feasibility and effectiveness of the GA-Powell .