Find in Library

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.