## 2 Materials and Methods
### The Positioning of the Proposed Model in the Existing Literature  
In order to clearly differentiate the proposed model from existing research approaches, Table 1 presents a structured comparison of relevant works from the literature. It summarises key features such as objectives, applied methods and the capabilities of the selected optimisation approaches referenced throughout this article. This comparison highlights the novelty of the proposed model, particularly in terms of its dynamic responsiveness, the integration of production planning and maintenance, and its specific orientation towards the requirements of single- and small-batch production. Unlike existing approaches, the model enables condition-based maintenance planning within a unified MIP-based optimisation framework. It incorporates critical factors such as machine wear, maintenance tasks and order assignments--an integration not yet addressed in the literature, especially for small-series production. In addition to the structured comparison above, several related approaches from the literature are discussed in more detail in the following. While [PERSON] et al. [21] propose a predictive maintenance scheduling model that accounts for machine conditions, the proposed model extends this by integrating both preventive and reactive strategies within a unified optimisation framework. Similarly, the optimisation approach of [PERSON] et al. [11] addresses dynamic conditions in flexible job-shop environments, but does not fully combine job scheduling and maintenance in a condition-based manner. The heuristic method of [PERSON] et al. [10] supports adaptive maintenance planning but lacks the mathematical integration of machine wear, task prioritisation, and scheduling flexibility. While [PERSON] et al. [14] present a dynamic rescheduling approach for flexible manufacturing in the event of machine failures, their model is limited to purely reactive measures and only aims to minimise lead time with the help of simulation and improved ICA. The model presented here, on the other hand, integrates both preventive and reactive maintenance strategies into a uniform MIP optimisation framework. It takes machine conditions, load factors and wear patterns directly into account in the planning logic. In this way, it not only reacts to faults at short notice, but also prevents them from occurring in the first place by proactively maximising machine availability. The asterisk in Table 1 indicates that, although the authors of [PERSON] et al. [14] formally address all three aspects, their model focuses solely on reactive maintenance and does not include preventive or condition-based strategies. Further differences can be observed in how production priorities and available time slots are handled. For instance, [PERSON] et al. [6] presents a capacity control model for a single machine aimed at minimising idle times, but it does not dynamically integrate maintenance into these time slots. In contrast, the proposed model makes targeted use of such free capacities to schedule maintenance in a way that improves both machine availability and production efficiency. Finally, models such as that in [PERSON] et al. [4] rely on static rules or threshold-based triggers for maintenance scheduling. The presented approach differs by enabling adaptive planning based on actual machine load and produc