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作 者:李浩平 杜昕毅 朱成彪 金朱鸿 陈心怡 于波涛 李景瑞 安宇婷 LI Haoping;DU Xinyi;ZHU Chengbiao;JIN Zhuhong;CHEN Xinyi;YU Botao;LI Jingrui;AN Yuting(College of Mechanical and Power Engineering,Three Gorges University,Yichang 443000,China)
机构地区:[1]三峡大学机械与动力学院,湖北宜昌443000
出 处:《太原理工大学学报》2024年第4期603-611,共9页Journal of Taiyuan University of Technology
基 金:国家重点研发计划资助项目(2018YFB1700801);湖北省水电工程施工与管理重点实验室(三峡大学)开放基金项目(2020KSD15)。
摘 要:【目的】在工厂实际加工中,由于各机器的健康状态参差不一,可引起机器故障继而影响加工施工时间。针对带有机器扰动的柔性作业车间的问题,为减小机器故障对生产计划的影响,提出了机器扰动、寻找断点的重调度模型,以最小加工时间、最短总延迟时间为优化目标构建了数学模型。【方法】提出改进灰狼优化算法(GWO-GA)作为全局优化搜索算法求解,为提高其灰狼算法的收敛速度,引入了工件编码迭代并加入自适应算子,使用遗传算法的POX交叉对机器编码进行迭代。【结果】针对钢琴制造企业实木车间数据进行验证,结果表明相对于遗传算法(genetic algorithm,GA)、NSGA、PSO-GA,改进灰狼算法求解本调度问题效率高,精度好,具有较好的实用价值。【Purposes】 In actual processing of factories, the varying health status of each machine can cause machine failures and subsequently affect the processing and construction time. This article focuses on the problem of flexible job shops with machine disturbances. In order to reduce the impact of machine failures on production planning, a rescheduling model for machine disturbances and breakpoint finding is proposed. A mathematical model is constructed with the optimization objectives of the minimum processing time and the minimum total delay time. 【Methods】 The improved grey wolf optimization algorithm(GWO-GA) is proposed as a global optimization search algorithm for solution. In order to improve the convergence rate of its gray wolf algorithm, the work piece coding iteration is introduced with an adaptive operator be added, and the POX crossover of genetic algorithm is used to iterate the machine coding. 【Findings】 The validation is conducted on the solid wood workshop data of piano manufacturing enterprises, and the results show that compared with genetic algorithm, NSGA, and PSO-GA, the improved grey wolf algorithm has advantages of high efficiency, good accuracy, and good practical value in solving this scheduling problem.
分 类 号:TH186[机械工程—机械制造及自动化]
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