<p>Bike-sharing systems offer an efficient and accessible option for short-distance transportation. However, the boom of bike-sharing systems also introduced challenges to urban transportation systems, such as congestion due to oversupplying bikes and low service quality caused by malfunctioning bikes. To address these challenges, government supervision has been conducted to improve operational orders in bike-sharing systems. For instance, the government may restrict the bike inventory in certain regions to avoid congestion caused by oversupplying bikes, and supervise the operational condition of the bike fleet to improve the satisfaction of users. Therefore, bike-sharing system operators should incorporate the influence of government supervision when formulating related decisions. This study introduces an optimization model for bike-sharing systems that simultaneously addresses depot location, inventory management, and bike rebalancing decisions under government supervision. Specifically, we consider government supervision aimed at controlling bike oversupply and managing the maintenance of malfunctioning bikes. Moreover, to address the uncertainty of travel demand and government supervision strategy, a distributionally robust optimization (DRO) model that incorporates both moment-based and sample-based information is developed and reformulated. The numerical results imply that the DRO model surpasses benchmark models. Furthermore, effect analyses are conducted, and we provide valuable managerial insights for government supervisors and bike-sharing system operators. For government supervisors, conducting the supervision could effectively mitigate the oversupply of bikes. While for bike-sharing system operators, we notice that the bike allocation restriction and malfunctioning bikes may significantly affect their profits. Accordingly, they should optimize the bike rebalancing operations to avoid such penalties and improve the operational profits.</p>