中文摘要：

为解决在扰动情况下的负荷不均和能耗问题,构建了以平均流经时间和能耗为优化目标的柔性作业车间调度模型。针对上述模型,设计了一种遗传算法和模拟退火算法相结合的GASA（Genetic and Simulated annealing Algorithm）算法,通过遗传算法的选择交叉变异操作产生一组新个体,对各个个体进行模拟退火过程,以避免陷入局部最优。针对柔性作业车间动态调度,在机器故障的扰动情况下,采用滚动窗口技术与GASA算法相结合的方法来求解动态调度问题。通过实验算例仿真,证明了算法的有效性。

英文摘要：

In order to solve the problem of uneven load and energy consumption under disturbance, a flexible job shop scheduling model with average flow time and energy consumption was constructed. Aiming at the above model a genetic and simulated annealing algorithm （GASA） was designed, which is based on the genetic algorithm and the simulated annealing algorithm. A new group of individuals were generated by genetic algorithm. And then the individual simulated the annealing process, in order to avoid falling into the local optimal. Aiming at the dynamic flexible job shop scheduling problem, the rolling window technique and GASA algorithm were combined and applied in the case of machine disturbance. The effectiveness of the algorithm was proved by the simulation of an instance.