In this paper, we analyze appointment systems involving heterogeneous customers, each requesting different services, with nonstationary arrival processes. The main goal is to identify server schedules that lead to good-performing systems, which we measure through the expected system time and the number of customer rejections. This decision problem arises in a number of applications and is especially relevant when certain service types dominate other service types. A key challenge in this analysis is the lack of closed-form analytical expressions that characterize the performance of the system. In this work, we construct a stylized optimization model based on a pointwise stationary approximation that emulates the original stochastic system. An analysis of the resulting stylized model comprised of a single customer type leads to key structural properties which we use to devise a globally convergent solution scheme that runs in polynomial time. This solution scheme is then generalized to the case of multiple customer types for two different formulations of the decision problem. To demonstrate the effectiveness of the proposed framework, we conduct a case study on Texas A&M University’s College and Psychological Services. Our results show that our optimal solutions substantially improve the performance of the system over current practices by reducing access time for critical mental health services by as much as 56%. Our analysis also identifies an easily implementable scheduling policy consisting of a single modification whose performance is within 10% of the more complex policies. History: Accepted by J. Paul Brooks, Area Editor for Applications in Biology, Medicine, & Healthcare. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0039 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0039 . The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

We examine optimal repair strategies for wind turbines operated under stochastic weather conditions. In-situ sensors installed at wind turbines produce useful information about the physical conditions of the system, allowing wind farm operators to make informed decisions. Based on the information from sensors, our research objective is to derive an optimal preventive maintenance policy that minimizes the expected average cost over an infinite horizon. Specifically, we formulate the problem as a partially observed Markov decision process. Several critical factors, such as weather conditions, lengthy lead times, and production losses, which are unique to wind farm operations, are considered. We derive a set of closed-form expressions for the optimal policy, and show that it belongs to the class of monotonic four-region policies. Under special conditions, the optimal policy also belongs to the class of monotonic three-region policies. The structural results of the optimal policy reflect the practical implications of the turbine deterioration process.