Maintenance planning for complex systems has still been a challenging problem. Firstly, integrating multiple dependency types into maintenance models makes them more realistic, however, more complicated to solve and analyze. Secondly, the number of maintenance decision variables needed to be optimized increases rapidly in the number of components, causing computational expensive for optimization algorithms. To face these issues, this thesis aims to incorporate multiple kinds of dependencies into maintenance models as well as to take advantage of recent advances in artificial intelligence field to effectively optimize maintenance polices for large-scale multi-component systems.
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Maintenance planning, Decision-making, Artificial intelligence, Reinforcement learning, Multi-agent systems
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