摘要
在机器故障后采用何种方式进行生产重调度,直接影响到柔性作业车间的生产效率和稳定性。为此,提出一种融合数据仿真、遗传优化与BP神经网络的重调度方式决策模型,以便在给定故障情形下经济、高效、快速地估计出最优重调度方式。首先,针对柔性作业车间,仿真生成不同机器故障情形下、各种重调度方式下的重调度方案,比较最大完工时间差值、工序结束时间差值和工序变动成本3个评价指标,将综合指标最小的重调度方式判定为给定情形下的最优方式,产生出带标签的大规模样本。在此基础上,构建基于遗传—神经网络的重调度方式决策模型,挖掘机器故障与重调度方式的内在联系,估计不同故障情形下的最优重调度方式。其中,遗传算法先用于确定BP神经网络结构,再用于优化权值和阈值。实验证明所提出决策模型能显著提升机器故障下柔性作业车间的决策效率与反应能力。
After a machine failure,the production rescheduling mode utilized shows a direct effect on the production efficiency and stability within flexible job shops.For this reason,a decision-making model combining data simulation,genetic optimization and BP neural network was proposed,so that the optimal rescheduling mode could be estimated economically,efficiently and quickly under a given fault situation.For the flexible job shops,a myriad of re-scheduling schemes were first generated for different simulated failure situations with all the predefined re-scheduling modes.By comparing three evaluation indicators,maximum completion time difference,completion time difference and process adjustment cost,one rescheduling mode with the smallest comprehensive index was labeled as the optimal mode in a given situation,and a large-scale labeled sample set was hereafter generated.On this basis,a decision-making model combining genetic optimization and neural network was proposed to explore the internal relationship between machine failures and rescheduling modes and to estimate the optimal rescheduling mode for each given fault circumstance.In this decision-making model,the genetic algorithm was employed to first determine the structure of the BP neural network,and then to optimize the weights and thresholds in the network.Experimental results showed that the proposed decision-making model could significantly improve the decision-making efficiency and response capability of the flexible job shops in a sudden fault.
作者
金鹏博
唐秋华
成丽新
张利平
JIN Pengbo;TANG Qiuhua;CHENG Lixin;ZHANG Liping(Key Laboratory of Metallurgical Equipment and Control Technology,Ministry of Education,Wuhan University of Science and Technology,Wuhan 430081,China;Hubei Provincial Key Laboratory of Mechanical Transmission and Manufacturing Engineering,Wuhan University of Science and Technology,Wuhan 430081,China)
出处
《计算机集成制造系统》
EI
CSCD
北大核心
2023年第11期3750-3761,共12页
Computer Integrated Manufacturing Systems
基金
国家自然科学基金资助项目(51875421,51875420)。
