| 引用本文: | 张怀权,黄春跃,梁颖,廖帅冬.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[J].控制理论与应用,2024,41(3):522~532.[点击复制] |
| ZHANG Huai-quan,HUANG Chun-yue,LIANG Ying,LIAO Shuai-dong.Load balance optimization model of surface-mount production line considering uncertain production factors[J].Control Theory and Technology,2024,41(3):522~532.[点击复制] |
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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 |
| Load balance optimization model of surface-mount production line considering uncertain production factors |
| 摘要点击 2904 全文点击 253 投稿时间：2022-07-01 修订日期：2022-12-12 |
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| DOI编号 10.7641/CTA.2023.20583 |
| 2024,41(3):522-532 |
| 中文关键词 负载平衡 蒙特卡洛法 神经网络 遗传算法 |
| 英文关键词 load balancing Monte Carlo method neural networks genetic algorithm |
| 基金项目 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. |
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| 中文摘要 |
| 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. |
| 英文摘要 |
| 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. |
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