Zhao Xufeng, Cai Jiajia, Mizutani Satoshi, Nakagawa Toshio
College of Economics and Management, Nanjing University of Aeronautics and Astronautics, NO.29, Jiangjun Avenue, Nanjing 211106, China.
Department of Business Administration, Aichi Institute of Technology, 1247 Yachigusa, Yakusa-cho, Toyota 470-0392, Japan.
J Manuf Syst. 2020 Jun 18. doi: 10.1016/j.jmsy.2020.04.003.
When a mission arrives at a random time and lasts for a duration, it becomes an interesting problem to plan replacement policies according to the health condition and repair history of the operating unit, as the reliability is required at mission time and no replacement can be done preventively during the mission duration. From this viewpoint, this paper proposes that effective replacement policies should be collaborative ones gathering data from time of operations, mission durations, minimal repairs and maintenance triggering approaches. We firstly discuss replacement policies with time of operations and random arrival times of mission durations, model the policies and find optimum replacement times and mission durations to minimize the expected replacement cost rates analytically. Secondly, replacement policies with minimal repairs and mission durations are discussed in a similar analytical way. Furthermore, the maintenance triggering approaches, i.e., replacement first and last, are also considered into respective replacement policies. Numerical examples are illustrated when the arrival time of the mission has a gamma distribution and the failure time of the unit has a Weibull distribution. In addition, simple case illustrations of maintaining the production system in glass factories are given based on the assumed data.
当任务在随机时间到达并持续一段时间时，根据运行单元的健康状况和维修历史来规划更换策略就成为一个有趣的问题，因为在任务执行期间需要可靠性，并且在任务持续时间内无法进行预防性更换。从这个角度来看，本文提出有效的更换策略应该是协作性的，即从运行时间、任务持续时间、最小维修和维护触发方法中收集数据。我们首先讨论基于运行时间和任务持续时间随机到达时间的更换策略，对这些策略进行建模，并通过分析找到最优更换时间和任务持续时间，以使预期更换成本率最小化。其次，以类似的分析方式讨论基于最小维修和任务持续时间的更换策略。此外，维护触发方法，即先更换和后更换，也被纳入各自的更换策略中。当任务到达时间具有伽马分布且单元失效时间具有威布尔分布时，给出了数值示例。此外，基于假设数据给出了玻璃厂生产系统维护的简单案例说明。
