This study investigates proactive scheduling for production spike mitigation in semiconductor manufacturing under temporary capacity fluctuation caused by production machine failures and recoveries. A phase-dependent control framework is developed with two mechanisms: machine allocation control and release control. Machine allocation control switches policies across production phases to reduce queue-time constraint (Q-time) violations and inter-product imbalance, while release control adjusts release quantities during predicted degradation periods to limit work-in-process (WIP) accumulation and waiting-time violations. Degradation periods are identified using either a data-driven failure-probability model based on machine data or a Weibull-based probabilistic model. Performance is evaluated in terms of throughput, cycle time, waiting time, variability, and rework rate. Both proactive strategies outperform the reactive baseline. Moreover, the data-driven approach reduces cycle time, waiting time and rework while maintaining stable throughput. These results demonstrate that degradation-aware proactive scheduling can mitigate production spikes and improve operational performance in non-stationary semiconductor manufacturing environments