摘要
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。
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.
出处
《中国机械工程》
EI
CAS
CSCD
北大核心
2015年第5期627-632,共6页
China Mechanical Engineering
基金
国家高技术研究发展计划(863计划)资助项目(2012AA040907)
关键词
加工时间扰动
柔性作业车间
鲁棒性
顺序搜索
processing time disturbance
flexible job-shop
robustness
sequential search
