Structured replacement policies for a system subject to random mission types
研究了系统在随机任务类型切换下的条件更换策略优化，利用任务切换时机进行状态监测，以最小化长期平均成本，并证明了最优策略的部分单调控制限形式。
Abstract This paper optimizes condition‐based replacement policies for a mission‐oriented system. The key challenge in our problem is that the system does not work under a fixed mission type but is subject to an infinite sequence of random types of missions assigned in a Markovian manner, which is realistic in many practical situations. The mission process modulates the deterioration process. Taking advantage of the opportunities when missions are switched, condition monitoring is conducted to support replacement decision‐making. This paper considers two practical scenarios in which the type of the next mission is either available or unavailable at each decision epoch. The objective is to determine the optimal replacement decisions for both scenarios that minimize their long‐run expected average cost rates. The optimization problems are analyzed in the framework of the Markov decision process. The optimal decisions of both scenarios are proven to be of partially monotone control‐limit forms. Near‐optimal policies with multilevel thresholds are provided for more convenient decision‐making. The policy iteration algorithm is modified for efficient optimization. A numerical example is used to demonstrate the feasibility of the proposed approach.