两阶段设备维护策略选择的灰靶决策模型
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作者单位:

1. 南京航空航天大学 经济管理学院,南京 211106;2. 南京航空航天大学 灰色系统研究所,南京211106

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E-mail: sfliu@nuaa.edu.cn.

中图分类号:

TP23

基金项目:

国家自然科学基金项目(72071111,71671091).


Grey target decision model for two-stage equipment maintenance strategy selection
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Affiliation:

1. College of Economic and Management,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China;2. Institute for Grey System Studies,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China

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    摘要:

    针对设备的最佳维护策略选择问题,首先提出6种设备运维目标,同时给出两阶段设备运维策略选择的加权智能灰靶决策模型的架构图以及建模算法流程;然后采用德尔菲调查法与层次分析法相结合确定不同目标的权重.结合某半导体面板制造企业的设备运行现场实际数据,对于成本型目标和适中型目标,分别采用相应的效果测度函数计算出设备在不同运行状态下的一致效果测度矩阵以及综合效果测度矩阵.通过运用两阶段设备维护的灰靶决策模型,最终得到设备不同状态下的最佳维护策略.所提出方法对正确选择半导体面板设备维护策略、提高设备运维效率、降低维护成本具有实际指导意义.

    Abstract:

    Aiming at the problem of selection the best maintenance strategy of equipment, firstly, the paper puts forward six equipment operation and maintenance objectives, and the architecture diagram and modeling algorithm flow chart of the intelligent weighted gray target decision model for the selection of two-stage equipment operation and maintenance strategy are proposed. Then Delphi survey method and analytic hierarchy process(AHP) are used to determine the weight of different objectives. Combined with the actual data of equipment operation in a semiconductor panel manufacturing enterprise, the corresponding effect measurement functions are used to calculate the consistent effect measurement matrix and comprehensive effect measurement matrix under different states for benefit objectives, cost objectives and moderate objectives. Using the multi-objective weighted intelligent grey target decision-making model, the optimal maintenance strategy under the production state of the equipment is obtained. The proposed method has practical guiding significance for correctly selecting the maintenance strategy of semiconductor panel equipment, improving the equipment operation and maintenance efficiency and reducing the maintenance cost.

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李强,刘思峰.两阶段设备维护策略选择的灰靶决策模型[J].控制与决策,2023,38(6):1712-1720

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  • 在线发布日期: 2023-05-13
  • 出版日期: 2023-06-20
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