加工时间不确定的柔性作业车间鲁棒调度方法

针对中小批量环境下加工时间不确定的柔性作业车间调度问题，采用冗余处理方法构建了以最大完工时间为目标的鲁棒调度模型。为降低算法的搜索规模和提高算法的求解速度，提出了顺序搜索机制，并设计两阶段遗传算法，分阶段获取冗余状态和最优结果。采用某柔性生产线的数据进行正交试验，优化了算法关键参数，并构建了柔性生产线仿真模型，对调度结果的鲁棒性和优化目标性能进行了分析。结果表明，该算法在目标性能和鲁棒性上都显著优于标准遗传算法，能有效处理加工时间不确定的柔性作业车间调度问题。

Robust Scheduling on Flexible Job Shop with Uncertain Processing Time

This paper investigated the flexible job-shop scheduling problem (FJSP) with uncertain processing time in a multi-type and low-volume environment. A minimax regret based robust scheduling model was built to minimize the makespan. A novel sequential search rule was put forward to reduce the calculation amount of the algorithm and a two stage genetic algorithm was designed to figure out the redundant and optimal solutions. Orthogonal test was designed to optimize significant parameters, and then, a simulation model was established to evaluate the robustness and objective performance of the algorithm. The results show the proposed algorithm has a better performance than genetic algorithm on flexible job-shop scheduling problem under uncertain and dynamic environment.