Abstract 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.

中文翻译：

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具有操作时间、任务持续时间、最少维修和维护触发方法的预防性更换政策

摘要 当任务随机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成为一个有趣的问题，因为在任务时需要可靠性并且无法更换。在任务期间采取预防措施。从这个角度来看，本文提出有效的更换策略应该是协作式的，从操作时间、任务持续时间、最少维修和维护触发方法收集数据。我们首先讨论具有操作时间和任务持续时间随机到达时间的替换策略，对策略进行建模并找到最佳替换时间和任务持续时间，以分析最小化预期替换成本率。第二，以类似的分析方式讨论了具有最少维修和任务持续时间的更换政策。此外，维护触发方式，即先更换和后更换，也被考虑到各自的更换策略中。当任务的到达时间具有伽马分布且单元的故障时间具有威布尔分布时，举例说明了数值示例。此外，还根据假设数据给出了维护玻璃厂生产系统的简单案例说明。当任务的到达时间具有伽马分布且单元的故障时间具有威布尔分布时，举例说明了数值示例。此外，还根据假设数据给出了维护玻璃厂生产系统的简单案例说明。当任务的到达时间具有伽马分布且单元的故障时间具有威布尔分布时，举例说明了数值示例。此外，还根据假设数据给出了维护玻璃厂生产系统的简单案例说明。