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考虑不确定生产因素的表面组装生产线负载平衡优化模型OA北大核心CSTPCD

Load balance optimization model of surface-mount production line considering uncertain production factors

中文摘要英文摘要

针对表面组装生产中的不确定因素造成企业订单完成时间滞后问题,本文设计并实现了一种考虑不确定生产因素的生产线负载平衡优化模型.首先,以不确定生产因素的历史样本数据作为随机模拟样本预估出不确定生产因素对订单完成造成的滞后时间;其次,优化元器件贴装工位分配方案并以任务完成作为触发事件模拟生产线实际运行得到动态生产计划;再次,根据动态生产计划计算出模型适应度值后,采用遗传算法对模型适应度值进行启发式寻优获得最优动态生产方案.最后,利用表面组装生产线试例对该模型进行验证,结果表明,该模型可准确预测产线各时段的生产任务、任务量及各器件贴装工位,有效提高了企业生产效率.

Aiming at the problem that uncertain production factors cause a lag in order fulfillment for surface-mount manufacturers,a load balance optimization model of production line considering uncertain production factors is designed.First,the historical sample data of uncertain production factors are used as a random simulation sample to predict the lag time of order completion caused by uncertain production factors.Second,after optimizing the component placement workstation allocation scheme,the task completion is used as the trigger event to simulate the actual operation of the production line to obtain a dynamic production plan.Third,after calculating the model fitness value according to the dynamic production plan,the genetic algorithm is used to heuristically optimize the model fitness value to obtain the optimal dynamic production plan.Finally,the model is validated by using a surface-mount production line test case.The result shows that the model can accurately predict the production tasks,task volume and placement stations of components in each period of the production line and can effectively improve the production efficiency of enterprises.

张怀权;黄春跃;梁颖;廖帅冬

桂林电子科技大学机电工程学院,广西桂林 541000成都航空职业技术学院信息工程学院,四川成都 610021||模式识别与智能信息处理四川省高校重点实验室,四川成都 610106

负载平衡蒙特卡洛法神经网络遗传算法

load balancingMonte Carlo methodneural networksgenetic algorithm

《控制理论与应用》 2024 (003)

多环境下微电子封装倒装芯片叠层焊点可靠性技术研究

522-532 / 11

国家自然科学基金项目(62164002),模式识别与智能信息处理四川省高校重点实验室基金项目(MSSB-2022-02),广西科技重大专项(桂科)项目(AA19046004),桂林电子科技大学研究生教育创新计划项目(2022YCXS008,2021YCXS009)资助.Supported by the National Natural Science Foundation of China(62164002),the Project of Key Laboratory of Pattern Recognition and Intelligent Information Processing of Sichuan(MSSB-2022-02),the Science and Technology Major Project of Guangxi Zhuang Autonomous Region Gov-ernment(AA19046004)and the Innovation Project of GUET Graduate Education(2022YCXS008,2021YCXS009).

10.7641/CTA.2023.20583

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