We study the robust production and maintenance control for a production system subject to degradation. A periodic maintenance scheme is considered, and the system production rate can be dynamically adjusted before maintenance, serving as a proactive way of degradation management. Optimal control of the degradation rate aims to strike a balance between the risk of failure and the production profit. We first consider the scenario in which the degradation rate increases linearly with the production rate. Different from the existing literature that posits a parametric stochastic degradation process, we suppose that the degradation increment during a period lies in an uncertainty set, and our objective is to minimize the maintenance cost in the worst case. The resulting model is a robust mixed‐integer linear program. We derive its robust counterpart and establish structural properties of the optimal production plan. These properties are then used for real‐time condition‐based control of the production rate through reoptimization. The model is further generalized to the nonlinear production–degradation relation. Based on a real production–degradation dataset from an extruder system, we conduct comprehensive numerical experiments to illustrate the application of the model. Numerical results show that our model significantly outperforms existing methods in terms of the mean and variance of cost rate when degradation model misspecification is presented.

Problem definition: Normal operations of a power system require that alternating current frequency be maintained at a nominal value, for example, 50 Hz, whereas severe deviation from this value due to power deficiencies can cause cascading generator trips. Maintaining the frequency requires adequate inertia and frequency regulation reserve, which are primarily provided by online generators. In daily operations, generators due for preventive maintenance must be taken offline, and thus an improper maintenance schedule could jeopardize frequency security, as exemplified by the recent Texas power blackout. However, this natural nexus between frequency security and maintenance has been overlooked largely in the literature. Methodology/results: We fill the gap by developing a long-term generator maintenance scheduling model that incorporates frequency security constraints with hourly fidelity to meet industrial standards. These constraints amount to scheduling adequate inertia and frequency regulation reserve by considering uncertain power deficiency and inertia from intermittent renewable energy. We hedge the uncertainties by employing a robust optimization approach in which historical data are used to construct ambiguity sets. This inevitably results in an ultra-large-scale robust model because of the hourly fidelity. We reformulate it as a large-scale, mixed-integer linear program. An algorithm based on the progressive hedging idea is proposed to decompose the model into subprograms that can be solved in parallel. An explicit-dual cutting-plane method for the subprograms and a novel lower bound for the model are developed to accelerate computation in each iteration. Compared with the standard progressive hedging algorithm and an L-shaped algorithm with strengthened Benders cuts, our algorithm is approximately 10 times faster and avoids the out-of-memory issues encountered by these benchmarks. Managerial implications: Integrating frequency security enforces generator maintenance to distribute more evenly across the planning horizon. This leads to a more stable maintenance crew size and a significant reduction in out-of-sample costs in our simulation using real data. Additionally, our study reveals that inertia is crucial for frequency security and that low-cost inertia resources like synchronous condensers can enhance frequency security. Funding: The research was conducted at the University of Macau, supported by the UM Grant SRG2025-00044-IOTSC and by FDCT support 001/2024/SKL (Y. Yang). This research was supported by the National Science Foundation of China 72471144 (Q. Sun). This research was supported by Singapore MOE AcRF Tier 2 Grant [A-8001052-00-00, A-8002472-00-00] (Z. Ye). The research was conducted at the Future Resilient Systems at the Singapore-ETH Centre, which was established collaboratively between ETH Zurich and the National Research Foundation Singapore. This research is supported by the National Research Foundation Singapore (NRF) under its Campus for Research Excellence and Technological Enterprise (CREATE) programme (J.C.-H. Peng, L.C. Tang, Z. Ye). Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0664 .