Abstract
Finding optimal maintenance policies for complex multi-component systems is a real-world challenge in the industry. This paper compares three maintenance policies for complex systems with non-identical components and economic dependencies in case of fault. Discrete event and Monte Carlo simulation are used to replicate fault occurrences, while a genetic algorithm is used to minimize the cost of maintenance by finding optimal groups of maintenance activities. Low total average maintenance cost and high average availability of the system are considered as desirable objectives and the capacity of the studied policies to achieve these goals is analyzed. None of the policies dominates the others (in a Pareto efficiency sense), thus making the policy choice context dependent and subject to decision makers' preferences.
Highlights
This paper studies a set of maintenance policy alternatives available for managing the maintenance of complex systems—policies with and without grouping of maintenance activities are considered
Minimal repair policy (MRP): A preventive maintenance activity is scheduled for all the components at time intervals of xi∗ (> 0) working hours based on the age
In the hope of helping to solve this challenge, we presented a comparative study of selected maintenance policies and a framework for analyzing them
Summary
This paper studies a set of maintenance policy alternatives available for managing the maintenance of complex systems—policies with and without grouping of maintenance activities are considered. Previous research in the field of maintenance policy research has produced a rather wide range of policies for the management of maintenance of complex multi-component systems [8], [9], [30], [45] These theoretical policy-models typically optimize the maintenance schedule with regards to several objectives and are able to integrate short-term information on system status. THE MODEL The model presented below is developed according to the five phases, rolling horizon approach, proposed by Wildeman et al [48], with the addition of activities duration, which can be summarized in the following steps: 1) Decomposition: determine the optimal frequency for maintenance of each component separately; the planning horizon is considered to be of infinite length during this step. The choice to analyze a series system reflects the approach of previous studies on the opportunistic maintenance policy [24], [49], [50], where the criticality of each component makes the opportunistic approach effective
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