عنوان مقاله [English]
In many industries, some of production capacity will be out of reach when a
machine deteriorates. Preventive Maintenance (PM) will enhance the machine condition, but it occupies some of production capacity. The PM will reduce the production capacity and will cause the customer's order delay if it is unnecessary, in contrast the probability of unexpected failure will be increased and will have the same or worst consequences if the PM is too late. One of the ways to deal with this problem is Condition-Based Maintenance with Discrete Monitoring (CBMWDM) whose main challenge is to find an optimal inspection scheme. If the time interval between inspections is too short, the inspection cost will be increased although it will diminish unnecessary PMs and unexpected failures. On the other hand, if the interval between inspections is too long, the sum of unexpected failure and backorder costs will be increased although the cost of inspection will be reduced. Hence, simultaneous planning of the inspections and PM actions should be emphasized. In this paper, a single product single machine system with Markovian deteriorating conditions and demand uncertainty is considered. The main objective is to integrate the inspections and maintenance planning in a tactical level and finite horizon that minimizes the average cost of inspections, maintenances, and backorders. For this purpose, a stochastic dynamic programming model is presented whose structure is dependent to appointed inspection scheme. The state variable of the model is an ordered pair whose components represent the demand and machine condition. The demand is a discrete random variable with arbitrary distribution and the machine condition will be determined after each inspection. Corresponding to each inspection scheme and for each outcome of the state variable, optimal PM decision is obtained and consequently the optimal inspection scheme is determined. The optimal inspection scheme and corresponding PM decisions determine simultaneous inspections and preventive
maintenance planning. The strategy of the new model is analyzed for a six-month horizon. Numerical results of the model show that the total average cost is
non-decreasing in machine state and demand. Secondly, at the time of inspection, the preventive maintenance should be executed at the same time or earlier if machine state is worse or demand is more.