Session 1: Maintenance and Reliability – Chair: Rommert Dekker
Sha Zhu (Erasmus University Rotterdam) – Spare parts inventory control based on maintenance planning
For many maintenance organisations, inspection-based maintenance tasks are the main source of spare part demand. Intermittency in spare part demand is caused both by an uneven distribution of maintenance tasks over time and by the uncertainty inherent in the unknown condition of assets until inspection. Intermittency severely complicates spare parts inventory control. In an attempt to partially overcome these complications, we propose to use the maintenance plan as a source of advance demand information. We propose a simple forecasting mechanism to estimate the spare part demand distribution based on the maintenance plan, and develop a dynamic inventory control method based on these forecasts. The value of this approach is benchmarked against state-of-the art time series forecast methods, using data from two large maintenance organisations in the Netherlands. We find that the proposed method can yield cost savings of 51% to 23% compared to the traditional methods.
Ayse Sena Eruguz (Erasmus University Rotterdam) – Optimizing usage and maintenance decisions for k‐out‐of‐n systems of moving assets
We consider an integrated usage and maintenance optimization problem for a k‐out‐of‐n system pertaining to a moving asset. The k‐out‐of‐n systems are commonly utilized in practice to increase availability, where n denotes the total number of parallel and identical units and k the number of units required to be active for a functional system. Moving assets such as aircraft, ships, and submarines are subject to different operating modes. Operating modes can dictate not only the number of system units that are needed to be active, but also where the moving asset physically is, and under which environmental conditions it operates. We use the intrinsic age concept to model the degradation process. The intrinsic age is analogous to an intrinsic clock which ticks on a different pace in different operating modes. In our problem setting, the number of active units, degradation rates of active and standby units, maintenance costs, and type of economic dependencies are functions of operating modes. In each operating mode, the decision maker should decide on the set of units to activate (usage decision) and the set of units to maintain (maintenance decision). Since the degradation rate differs for active and standby units, the units to be maintained depend on the units that have been activated, and vice versa. In order to minimize maintenance costs, usage and maintenance decisions should be jointly optimized. We formulate this problem as a Markov decision process and provide some structural properties of the optimal policy. Moreover, we assess the performance of usage policies that are commonly implemented for maritime systems. We show that the cost increase resulting from these policies is up to 27% for realistic settings. Our numerical experiments demonstrate the cases in which joint usage and maintenance optimization is more valuable.
Rob Basten (Eindhoven University of Technology) – Maintenance optimization for multi-component systems with a single sensor
We consider a multi-component system in which a condition parameter (e.g., vibration or temperature) is monitored by a single sensor that gives system level information. The outcome of monitoring indicates whether the system is functioning properly, is defective, or has failed. However, the condition signal does not reveal which component in the system is defective or has failed. The decision maker needs to infer the exact state of the system from the current condition signal and the past data, in order to decide when to intervene for maintenance. A maintenance intervention consists of a complete and perfect inspection followed by component replacement decisions. We model this problem as a partially observable Markov decision process.