Python Reliability-Driven Production Scheduling Optimization for UHV Converter Transformers Under Dynamic Disturbances

Ultra-High Voltage (UHV) converter transformer manufacturing is characterized by long production cycles, complex process interactions, and frequent disturbances, which reduce scheduling reliability and production efficiency under traditional manual planning. To address these challenges, this study proposes a reliability-driven intelligent scheduling framework for UHV converter transformer production. First, key disturbance factors are identified through expert consultation, and a dataset comprising 238 UHV converter transformers from major domestic manufacturers is established. Second, an improved Expected Risk Score (ERS) method integrated with fuzzy rough number theory is developed to evaluate disturbance importance while reducing subjectivity in weight determination. Third, quantitative relationships between disturbance intensity and schedule deviation are established. Fourth, a Monte Carlo simulation-based reliability evaluation model is constructed to assess schedule feasibility under uncertainty. Finally, a hybrid scheduling algorithm incorporating reliability evaluation is proposed to improve schedule robustness while balancing production efficiency and resource utilization. Results show that the proposed method reduces process idle time and schedule adjustment frequency, improves equipment workload balance, and significantly enhances scheduling reliability and operational efficiency. The proposed framework provides an effective decision-support tool for disturbance-aware scheduling in complex equipment manufacturing.