## 5 Real-Time Scheduling Methods for Flexible Manufacturing Systems  
Trends towards customized products and shorter delivery times lead to a major challenge for manufacturing factories automation and management systems. The increase in product variants introduces significant non-production times due to system reconfiguration and set up needs at each product changeover. Besides internal production uncertainties, external factors such as a varying customer demand, supply chain ruptures or even raw material price dynamics increase the complexity level of the manufacturing configuration, planning and scheduling tasks [75]. To cope with these challenges, today's manufacturing companies need not only high-performance and flexible processes but also robust concepts to handle foreseen variances and unforeseen disturbances with minimal impact and small additional production costs [76,77]. Approaches to raise the production flexibility can range from improvements and development of adaptive work cells with self-adaptive control functions [78,79] to optimized production planning and scheduling solutions [80]. The mapping and optimization of these increasingly complex production processes is an important field of research. Recent works underline the relevance of production scheduling and show a tendency towards the following topics in the last years [81]:  
* Self-organization of resources;
* Self-regulation of the production process;
* Self-learning capacities of the overall manufacturing industrial system. These topics highlight the importance of autonomous and self-optimizing scheduling approaches to handle the overall growing production dynamics. Frequently applied scheduling optimization objectives are the maximization of machine utilization and the minimization of the lead time to produce a product [82]. Additionally, an increased sensibility and awareness of the climatic impact lead to further requirements such as the minimization of utilized resources and the reduced emission of greenhouse gases. Once the scheduling objectives are defined, various approaches exist to implement a job shop scheduling policy. A general classification divides them into centralized, distributed or more recent hybrid scheduling and control architectures (HCA) [83]. These hybrid structures aim to combine the advantages of both approaches, while reducing their drawbacks. State-of-the-art architectures are multi-agent systems (MAS), holonic manufacturing systems (HMS) or product-driven systems (PDS) [83]. Another classification presented by [PERSON] et al. [84] groups the job shop scheduling models by the problem space in the following five types: basic type job shop scheduling problem (JSP), flexible JSP, multi-resources FJSP model (MrFJSP), multi-plants-based MrFJSP (MpFJSP) and MpFJSP with smart factory. Independently of the scheduling architecture or model, dynamic scheduling approaches are required to cover and react on production events in real time. Events can be classified in resource- or job-related events [85]. Thereby resource-related events include process-linked incidents, as for example machine breakdowns or material shortages. Examples for job-related events are job cancellation or a change in priorities. Handling these dynamic and unforeseen events, while considering constraints and variations of each production process, is a key challenge of industrial production scheduling solutions. To cope with the challenge, accurate production models are required to obtain realistic results. However, with increasing model complexity, computation times and resulting optimization times increase significantly, limiting the real-time application potential of advanced scheduling methods. To overcome these issues, new technologies and algorithms are required to model and optimize the industrial operation scheduling problem. Promising approaches are dynamic HCA with self-adaptive mechanisms to improve the agility and reactivity [86]. [PERSON] et al.