Maintenance optimization in industry 4.0

•Knowledge, Information and Data that can be exploited for maintenance optimization are analyzed.•Traditional and new emerging optimization criteria are critically discussed.•Maintenance aspects to be optimized are reviewed.•Challenges and trends of maintenance optimization in Industry 4.0 are identified.•Metaheuristic search algorithms and reinforcement learning-based approaches emerge as most promising for maintenance optimization. This work reviews maintenance optimization from different and complementary points of view. Specifically, we systematically analyze the knowledge, information and data that can be exploited for maintenance optimization within the Industry 4.0 paradigm. Then, the possible objectives of the optimization are critically discussed, together with the maintenance features to be optimized, such as maintenance periods and degradation thresholds. The main challenges and trends of maintenance optimization are, then, highlighted and the need is identified for methods that do not require a-priori selection of a predefined maintenance strategy, are able to deal with large amounts of heterogeneous data collected from different sources, can properly treat all the uncertainties affecting the behavior of the systems and the environment, and can jointly consider multiple optimization objectives, including the emerging ones related to sustainability and resilience.