Fault-Resilient Manufacturing Scheduling with Deep Learning and Constraint Solvers

As edge computing environments become increasingly dynamic, the need for efficient job scheduling and proactive fault prevention is becoming paramount. In such environments, minimizing machine downtime and maintaining productivity are critical challenges. In this paper, we propose an integrated approach to scheduling optimization that combines deep learning-based fault prediction with Satisfiability Modulo Theories (SMT)-based scheduling techniques. The proposed system predicts fault probabilities for machines in real time by leveraging operational state features such as temperature, vibration, tool wear, and operating hours. These fault predictions are then used as inputs to the SMT solver, which dynamically optimizes job scheduling. The system ensures task completion within deadlines while minimizing fault risks and optimizing resource utilization. To achieve this, the deep learning model continuously updates fault probabilities through a rolling prediction mechanism, allowing the scheduling system to proactively adapt to changing machine conditions. The SMT solver incorporates these predictions into its optimization process, ensuring that the schedule dynamically reflects the latest system state. The proposed method has been evaluated in simulated production line scenarios, demonstrating significant reductions in machine faults, improved scheduling efficiency, and enhanced overall system reliability. By integrating predictive maintenance with optimization techniques, this research contributes to the development of robust and adaptive scheduling systems for dynamic production environments.