基于混合遗传算法的车间调度问题的研究

Study on JSP Based on Hybrid-Genetic Algorithm

提出了在柔性生产环境下基于遗传算法与模拟退火算法混合的动态调度算法,充分发挥遗传算法良好的全局搜索能力和模拟退火算法有效避免陷入局部极小的特性,有很好的收敛精度,并且能够在扰动发生后提供新的调度计划;通过交叉,变异等遗传操作、得到目标的最佳或次优解,最后对算法进行了仿真研究,仿真结果表明该算法是可行的,与传统的调度算法相比,其优越性是明显的。

A GASA hybrid genetic algorithm is proposed for job shop scheduling.It is using GA excellent whole search ability and simulated annealing which is efficient to avoid getting into part minimum.It is capable of generating alternative schedule after uncertain disturbance takes place on a job shop.An optimum or second optimum solution is obtained by using multiple crossover and mutation operators.Simulation results based on some flow shop scheduling benchmarks show that the GASA is feasible,efficient and superior to the simple GA.