# Fault-Resilient Manufacturing Scheduling with Deep Learning and Constraint Solvers
## 8 Conclusions  
In this paper, we have proposed a hybrid approach that integrates deep learning and constraint solvers to address the challenges of proactive fault management and job scheduling in edge computing environments. Our framework combines the predictive capabilities of a deep learning model with the optimization power of a constraint solver, enabling dynamic and real-time decision-making. By leveraging machine state data such as tool wear, temperature, vibration, and operating hours, the proposed system effectively predicts machine fault probabilities and optimizes job scheduling to minimize risks and enhance operational efficiency. The main contributions of our work include the following:  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Metric** & **Average (s)** & **Standard Deviation (s)** & **Range (s)** \\ \hline Fault Prediction Time & 0.01875 & 0.00171 & 0.0169–0.0210 \\ Scheduling Optimization Time & 0.02038 & 0.00548 & 0.0126–0.0333 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance metrics for fault prediction and scheduling optimization.  
* Seamless Integration of Prediction and Optimization: Our approach bridges fault prediction with scheduling optimization, utilizing real-time fault probabilities predicted by a deep learning model to guide a constraint-based scheduler. This integration enables proactive scheduling adjustments based on evolving machine conditions.
* Accurate Fault Prediction: The 1D-CNN model used in this paper demonstrated robust performance in predicting machine faults by analyzing temporal patterns in state data, providing a reliable basis for maintenance and scheduling decisions.
* Optimized Scheduling: The constraint solver efficiently assigned jobs to machines, ensuring deadlines were met while minimizing fault risks and operational costs. This contributed to reduced downtime and improved resource utilization.
* Adaptability and Scalability: The proposed framework is highly adaptable to various production scenarios. Notably, it supports retraining of the deep learning model as machine behaviors evolve and scales efficiently to larger systems with diverse job requirements.  
Through comprehensive experiments and case studies, the proposed framework has been shown to significantly reduce machine fault risks and production delays while maintaining system deadlines. By integrating real-time machine state data, the system demonstrates the ability to adapt to dynamic conditions, ensuring reliable and efficient production line operations.  
Future research will focus on extending the model to capture more complex machine behaviors and incorporating additional factors such as energy efficiency and resource allocation into the optimization framework. Enhancing the scalability of the constraint solver to handle larger and more intricate production environments will also be a priority. Additionally, we aim to explore the application of this framework in other domains where predictive decision-making and scheduling are critical to operational success, such as logistics and healthcare.  
This research was funded by the New Faculty Financial Program at Changwon National University in 2023.  
Not applicable.  
The dataset is available on request from the corresponding author.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
SMIT  
Satisfiability Modulo Theories  
1D-CNN  
One-Dimensional Convolutional Neural Network  
GRU  
Gated Recurrent Unit  
LSTM  
Long Short-Term Memory  
IoT  
Internet of Things  
RMSE  
Root Mean Squared Error  
MAE  
Mean Absolute Error  
SPT  
Shortest Processing Time  
LPT  
Longest Processing Time  
MLB  
Minimum Load Balancing