Optimal control-limit maintenance policy for a production system with multiple process states

AbstractHighlights •Take process states and spare parts as part of maintenance decisions.•Include virtual age to help the indication of machine state.•Consider the randomness of imperfect maintenance effect and spares lead time.•Consider the impact of process state and virtual age on production cost.•Propose an effective modified policy-iterative algorithm for cost optimization. AbstractIn this paper, a new control limit maintenance policy is proposed for a production system with multiple process states. The process state can be obtained by quality information or other measurable indicators from the production process, when the process state by itself provides an insufficient indication or measure of the actual machine state. This can make the maintenance activities difficult to implement. Alternatively, machine virtual age can indicate the actual machine state and the maintenance effect on the machine state. Therefore, machine virtual age is used to compensate for the possible weakness of process state as an indication of machine state, and it is also used as part of maintenance decisions for more effective maintenance activities. At each inspection time, maintenance decisions are made for spare parts ordering and maintenance activities. The maintenance activities which are considered in this paper are replacement and two types of imperfect maintenance. The new maintenance planning model considers the different maintenance levels and their effects on system performance. Moreover, different maintenance cost and durations are considered. The system and associated maintenance policy are modeled by a discrete-time Markov decision process, and the long-term expected cost rate is investigated. A modified policy-iterative algorithm is then proposed to accelerate the search speed effectively during cost optimization. Compared to a traditional algorithm, the modified algorithm checks the effects of some possible parameters, rather than all parameters during the policy-iteration process. Simultaneously, it assures that the checked parameters are better than those unchecked. Finally, a numerical example is presented to illustrate the proposed method, and sensitivity analyses of important parameters are also implemented.