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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。
In order to solve the problem of load unbalance and energy consumption in the case of disturbances, a flexible job shop scheduling model with the objective of optimizing the average flow-through time and energy consumption was constructed. A Genetic Algorithm and Simulated Annealing Algorithm (GASA) algorithm is designed to solve the above model. A new set of individuals are generated by genetic crossover mutation operation, and each individual is simulated and annealed Avoid falling into the local optimum. Aiming at the dynamic scheduling of flexible work shop, the dynamic scheduling problem is solved by the combination of rolling window technology and GASA algorithm in the case of machine disturbance disturbance. The simulation results show that the algorithm is effective.