In view of the high total cost of semiconductor manufacturing assets, respective equipment needs to be as productive as possible. To avoid needless idling and unnecessary downtime, scheduling and maintenance strategies are important in practice. This paper presents a novel approach to reduce the substantial setup costs inherent to ion implantation by deriving scheduling constraints based on current equipment conditions. Consequently, a supervised learning pipeline is established that utilizes built-in sensors and process target data to accurately predict setup costs. The derived constraints are integrated into scheduling, thereby enhancing its efficiency through dynamic dispatching adaptations. The application of our method is projected to significantly improve equipment availability by avoiding more than 100 hours of potential downtime annually.