Joint optimization of condition-based maintenance and EPQ based on the random coefficient growth model

LIU Xuejuan, FENG Zhipeng

Systems Engineering - Theory & Practice ›› 2019, Vol. 39 ›› Issue (1) : 251-258.

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中国科学院数学与系统科学研究院期刊网
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Systems Engineering - Theory & Practice ›› 2019, Vol. 39 ›› Issue (1) : 251-258. DOI: 10.12011/1000-6788-2018-0365-08

Joint optimization of condition-based maintenance and EPQ based on the random coefficient growth model

  • LIU Xuejuan1, FENG Zhipeng2
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Abstract

A joint optimization model of economic production quantity and condition-based maintenance policy is proposed in this paper, considering the situation that the production plan and maintenance schedule share the same machine. Using the random coefficient growth model to describe the degenerated condition, the condition monitoring is carried out when a lot is finished. The observed condition composed of two parts, the actual deterioration condition and the random error, once the condition information observed is equal to or higher than the preventive maintenance level, or the actual condition is equal to the failure level, the machine should be renewed. The cost and the length model in the renewal cycle are proposed, then the expected cost per unit time is modeled based on the renewal reward theory. The optimal condition of preventive maintenance level and production time of one lot can be calculated by minimizing the expected cost per unit time. Numerical examples are presented based on the data collected from a steel mill, the results are consistent with the actual situation.

Key words

condition-based maintenance / economic production quantity / the random coefficient growth model / renewal reward theory

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LIU Xuejuan , FENG Zhipeng. Joint optimization of condition-based maintenance and EPQ based on the random coefficient growth model. Systems Engineering - Theory & Practice, 2019, 39(1): 251-258 https://doi.org/10.12011/1000-6788-2018-0365-08

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Funding

National Natural Science Foundation of China (71601019); Humanities and Social Sciences Foundation of Ministry of Education of China (16YJC630174)
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