具有生态意识的二维视情维修决策研究
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上海大学悉尼工商学院

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N945.16

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国家自然科学基金项目


Two-dimensional Condition-based Maintenance Decision-making with Environmental Awareness
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    摘要:

    为了避免预防性维修的决策判断过于片面,本文首先将系统劣化状态和能源效率指标相结合,建立了具有生态意识的二维视情维修决策模型,即当系统的劣化状态超过其阈值或者能源效率指标超过其阈值时(两者发生其一即可),对系统进行预防性维修;然后将系统运行过程中的能源消耗成本纳入总成本中,并以单位有用产出的平均期望成本为目标函数建立优化模型;最后利用蒙特卡洛仿真和模拟退火算法进行算例分析。结果表明:与传统的只基于劣化状态或者能源效率指标的一维视情维修决策模型相比,本文新提出的二维视情维修决策模型更优,不仅可以降低企业维修成本,而且可以节约能源,满足可持续发展的时代要求。

    Abstract:

    In order to solve the decision variable unilateral problems of existing maintenance strategy, this paper establishes the environmental aware two-dimensional condition-based maintenance optimization model. These two dimensions are consciousness state of degradation and energy efficiency indicator. First of all, when inspect the system periodically, preventive maintenance can be decided according to the deteriorating state of the system or energy efficiency indicator. That is to say, if the deterioration of the system exceeds its preventive threshold or energy efficiency indicator is above its preventive threshold (whichever occurred first), preventive maintenance of the system is carried out. Then, the cost resulted by energy consumption is considered into the total cost, and the long-term expected cost per useful output is established as the objective function. Finally, the optimal condition-based maintenance policy can be obtained by Monte Carlo simulation and simulated annealing algorithm in numerical analysis. The results justify the superiority of the new proposed two-dimensional condition-based maintenance decision-making model. Compared to traditional one-dimensional condition-based maintenance decision-making model which is only based on the deterioration level or the energy efficiency indicator, the new proposed model is a better decision-making model, which can not only reduce the maintenance cost but also save the energy consumption. This new proposed model promotes the sustainable development in new era.

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姜爱萍,王园园.具有生态意识的二维视情维修决策研究[J].,2019,(9).

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  • 收稿日期:2018-06-12
  • 最后修改日期:2019-04-19
  • 录用日期:2018-09-04
  • 在线发布日期: 2019-07-01
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