期刊
MATHEMATICS
卷 10, 期 18, 页码 -出版社
MDPI
DOI: 10.3390/math10183229
关键词
working cycle; warranty; rebate; random hybrid age replacement; cost rate
类别
资金
- Characteristic Innovation Projects of Colleges and Universities in Guangdong Province [2021WTSCX081]
- Base and Basic Applied Study of Guangdong Province [2020A1515011360]
- National Natural Science Foundation of China [71871181, 72161025]
This paper proposes a random free repair warranty with rebate and charge (RFRW-RC) and a random hybrid age replacement (RHAR) to ensure system reliability throughout the whole life cycle. The models consider factors in both the warranty stage and post-warranty stage, providing manufacturers with important decision-making insights.
Facilitated by advanced digital technologies, reliability managers can monitor system working cycles during the whole life cycle. Such a technological realization can help reliability managers ensure system reliability in real time by monitoring working cycles. In this paper, by incorporating a limited random working cycle, rebate and charge into warranty theory, a random free repair warranty with rebate and charge (RFRW-RC) is devised to ensure system reliability during the warranty stage. Under RFRW-RC, the rebate removes manufacturers' responsibility for continuing to ensure system reliability, while the charge is a support where manufacturers continue to ensure system reliability. The warranty cost of RFRW-RC is derived, and a random discrete free repair warranty (RDFRW) is presented by simplifying RFRW-RC. By mixing random age replacement last (RARL) and classic age replacement (CAR), a random hybrid age replacement (RHAR) is designed in order to ensure system reliability during the post-warranty stage. In such an RHAR, RARL is applied to extend the replacement time during the post-warranty stage in order to maximize the remaining life of the system through warranty, and CAR is used to lower the maintenance cost of the system through warranty. The cost rate of RHAR is modeled, and the cost rate of RDFRW is offered as well by discussing parameter values. The decision variable is optimized by minimizing the cost rate model. The properties of the presented models are explored from numerical perspectives.
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