## 1 Introduction  
In view of the challenges of Industry 4.0, the manufacturing industry faces a multitude of changes [1, 2]. Particularly affected is order-based single zu- and small-batch production, which features a high variety of variants, small batch sizes and a heterogeneous machine park [3]. In single- and small-batch production, the high variety of variants leads not only to complex production processes but also to uneven machine utilisation [4]. Different material properties, specialised processing steps and varying process chains result in certain machines being used more while others remain underutilised. The increasing demand for customer-specific products, combined with complex production processes and the requirement for reliability, necessitates new approaches in production control to flexibly and efficiently respond to dynamic conditions [3, 5]. Unforeseen events such as machine failures, sudden demand fluctuations, changes in delivery dates,or material shortages often cause unexpected adjustments in the production schedule [6]. As a result, unused time slots emerge that are not efficiently utilised, leading to a waste of valuable resources such as time and personnel [5]. In response to these challenges, the focus of recent research has been on the integration of production control and maintenance strategies, particularly in single-part and low-volume production environments. A previous systematic literature review [3] identified relevant research gaps and pointed out the need for dynamic and flexible optimisation approaches. It highlighted the limitations of current models in dealing with contingencies and the potential for a combination of preventive and reactive maintenance in operational planning. Scientific databases such as Dimensions.ai and Scopus formed the foundation for this literature review. The findings indicate that while existing studies propose models for integrating maintenance strategies into production planning, they often fail to address the dynamic requirements of single- and small-batch production [7]. In particular, the integration of preventive and reactive maintenance within an operational planning horizon remains under-researched. The literature further shows that while existing approaches consider various aspects such as preventive or reactive maintenance strategies, they are often designed for stable production environments like mass production [7; 8]. As a result, short-term dynamic adjustments remain insufficiently supported. Existing models and algorithms, which base their calculations on fixed maintenance intervals, lack the flexibility to effectively handle unplanned events [9]. Previous research has developed various methods to integrate production control and maintenance strategies, including genetic algorithms for minimising lead time [10], multi-objective optimisation problems [11], and dynamic control algorithms [6]. However, these approaches often do not fully address short-term dynamic adjustments. The approach presented in this work builds on an extended job-shop scheduling model, which closely integrates preventive maintenance strategies with production control. In particular, it considers complex machine chains and dynamic events such as sudden machine failures or short-term order arrivals, which are highly relevant in single- and small-batch production. The model combines both time-based and condition-based maintenance measures, enabling flexible adjustments to machine allocation in response to unforeseen events. A key difference from existing approaches lies in the dynamic integration of maintenance measures into the production schedule. As a result, unused time slots serve either for preventive maintenance or for optimising the sequence of production orders, minimising downtime and improving resource utilisation. In contrast to traditional models that assume fixed maintenance intervals [12; 13], the proposed approach allows for the dynamic scheduling of maintenance activities based on machine conditions and operational requirements.