计算机集成制造系统 ›› 2016, Vol. 22 ›› Issue (第12期): 2800-2808.DOI: 10.13196/j.cims.2016.12.008

• 产品创新开发技术 • 上一篇    下一篇

考虑学习曲线的精益生产制造系统双重资源配置方法

肖倩乔,李益兵+,左少雄,杜百岗,郭顺生   

  1. 武汉理工大学机电工程学院
  • 出版日期:2016-12-31 发布日期:2016-12-31
  • 基金资助:
    国家自然科学基金资助项目(71171154);湖北省科技支撑计划资助项目(2014BAA032)。

Dual-resource configuration of manufacturing system based on lean production considering learning curve

  • Online:2016-12-31 Published:2016-12-31
  • Supported by:
    Project supported by the National Natural Science Foundation,China(No.71171154),and the Science and Technology Supporting Plan of Hubei Province,China(No.2014BAA032).

摘要: 为了有效配置设备与人员资源以提高车间单元化生产的产能与效率,针对劳动力资源的消耗利用是一个复杂的认知过程,提出一种考虑学习曲线的基于精益指标的制造系统双重资源配置方法。以产品制造单元化生产方式为主线,将生产成本、生产时间和加工质量作为车间精益优化目标,考虑员工学习曲线对生产过程的动态作用,研究人机两类资源与员工学习能力的动态变化关系,获得优化配置的数学模型,并给出多目标优化模型的求解方法。以纺机企业锡林组件加工为例具体分析了其求解过程,结果表明,用所提出的模型与方法求解多目标约束下的资源配置问题是可行和有效的。

关键词: 双重资源, 学习曲线, 精益生产, 纺机企业, 多目标优化

Abstract: To effective allocate the equipment and employee for improving the capacity and efficiency of workshop-unitized production,aiming at the problem that the consumption and usage was a complex cognitive process,a dual-recourse configuration of manufacturing resources based on lean indicators by considering the learning curve was put forward.The product manufacturing-unitized production was taken as the main line,and the dynamic reactive of employee learning curve on production process was considered to research the dynamic relationship between human-machine resource and employee learning capacity,in which the production-cost,production-time and production-quality were taken as the optimal objects of workshop lean production.The optimized allocation model was obtained,and the solution for multi-objective optimization configuration was suggested.The solution process of cylinder components production in textile enterprises was analyzed,and the result showed that the proposed model and method were effective for solving multi-objective constrained resources allocation problem.

Key words: dual-resource, learning curve, lean production, textile manufacturing enterprise, multi-objective optimization

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