An integrated scheduling model for single-machine systems considering fatigue dynamics, speed control, maintenance, and learning-based performance
提出一个集成调度模型，结合操作员疲劳动态、学习效应、预防性维护和速度相关排放，通过遗传算法求解，数值实验表明整合休息、维护和学习能显著缩短完工时间、降低工作量并控制排放。
• Proposes a single-machine scheduling model combining fatigue dynamics, learning, preventive maintenance, minimal repair, and speed-dependent emissions. • Models operator fatigue and learning over time, while enforcing a CO 2 emission cap linked to machine speed. • Includes proactive, preventive, and reactive minimal repair strategies with Weibull-based machine aging. • A tailored GA efficiently solves the model, utilizing fatigue-aware decoding and local improvement. • Numerical experiments show that integrating rest, maintenance, and learning significantly improves the makespan, reduces workload, and maintains emissions within limits.