## 2 Materials and Methods
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Additionally, the job-shop optimisation ensures on-time order fulfilment by dynamically adapting to fluctuating production conditions. Compared to existing approaches, the model stands out due to its adaptability to varying machine loads and production priorities. This flexible scheduling approach enables strategic maintenance planning, reducing production disruptions and enhancing overall efficiency. The priority-based allocation of production orders and maintenance activities \begin{table}
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**Criterion** & **Traditional Method (Static Planning)** & **Proposed Model (Dynamic Planning)** \\ \hline Machine Availability & Fixed maintenance intervals without considering the actual machine condition. Unexpected failures lead to unplanned downtime. & Maintenance is scheduled into available time slots. Dynamic adjustments based on wear reserves reduce unplanned downtime. & Maintenance is scheduled into available time slots. Dynamic adjustments based on wear reserves reduce unplanned downtime. \\ Delivery Reliability & Production planning is independent of maintenance planning, which can cause delays due to unexpected machine failures. & Integrated planning avoids conflicts between maintenance and production. Maintenance is flexibly scheduled to minimise delays. & Unplanned downtime is scheduled to minimise delays. \\ Utilisation of Free Machine Capacity & Maintenance slots are not optimised, leading to unused production capacity. & Available maching capacity is actively used for maintenance, optimising production utilisation. \\ Reaction to Unplanned Events & Static planning makes short-term adjustments difficult when dealing with machine failures or changes in orders. & Dynamic optimisation enables flexible responses to machine conditions and production requirements. \\ Maintenance Strategy & Preventive maintenance based on fixed time intervals, independent of actual machine usage. & Combined strategy with time-based intervals, condition-based scheduling and reactive measures. \\ Wear Assessment & No direct influence of job types on machine wear, as fixed maintenance intervals are used. & Wear is individually assessed based on job workload and process parameters, allowing for a more accurate prediction. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: A comparison between traditional static planning and the proposed dynamic planning model. The model optimises machine availability and delivery reliability through proactive maintenance planning and adaptive job-shop optimisation. ensures a high degree of flexibility, particularly in volatile production environments. However, scalability remains a challenge, especially for large problem instances. For larger problem instances, computational efficiency could be improved using more advanced solvers or heuristic methods, as discussed in the Limitations of the Model section. The computational effort could be reduced by leveraging advanced optimisation techniques, such as heuristics or AI-driven predictive scheduling. The integration of high-performance solvers, machine learning algorithms or heuristic optimisation methods could further improve scalability and computational efficiency. Additionally, sensitivity analyses could help assess the model's robustness against variations in input parameters, such as fluctuating machine wear rates. Furthermore, the accuracy of results heavily depends on precise input data, necessitating continuous monitoring and updating of machine conditions. Future research could focus on expanding the model with AI-based predictive processes, adaptive maintenance strategies and deeper integration with real-time production control systems. Further validation through industrial case studies could provide valuable insights into its practical applicability and operational impact. Real-world implementation would help determine the necessary adjustments to maximise its effectiveness in a production setting.