Feldsynchrone Ablaufplanung dynamischer Fertigungsprozesse mit Techniken des maschinellen Lernens [kumulative Dissertation]

Current solutions for holistic and real-time planning of dynamic manufacturing processes are reaching their limits. This is particularly applicable to complex sociotechnical production environments with flexible material flows as well as undetermined events and fluctuations. Methods of optimization under uncertainty are very computationally intensive and crucial interactions with the real world are insufficiently considered. This lack of field synchronicity reduces the quality of production schedules, leads to manual efforts firefighting), and has a negative impact on the logistical performance. The present work is based on four journal articles that demonstrate novel methods and models for improving field-synchronous scheduling. Through the combination of instruments from operations research and machine learning, generic and predictive algorithms are developed to improve the efficiency and effectiveness of planning procedures. The findings suggest that regression models can replace computation-heavy stochastic simulations in obtaining robustness metrics. Additionally, using reinforcement learning, uncertainty-robust and realistic production schedules for human-centered manufacturing can be generated in a short time. For this purpose, discrete simulation models are used, which are data-driven initialized based on a general control logic. The algorithms can be integrated into a virtual factory, which serves as a digital representation of the real world and is the basis for smart and field-synchronous scheduling systems. In this context, a prototype distributed system for the planning of dynamic manufacturing processes can be presented, which is being tested by industry research partners and further developed in collaboration. Beyond the publications, further research needs can be derived. In order to ensure the transferability of the methods, they need to be evaluated in the context of additional and more comprehensive environments. From a scientific and practical perspective, it is a crucial challenge to develop holistic and proactive scheduling systems that orchestrate a comprehensive set of data-driven analysis and decision-making processes. In this regard, the presented methods and models need to be further developed and integrated into a generic overall concept. The work identifies four focus areas that future research should address in an interdisciplinary manner: (1) Generic simulation models, (2) Human-centered optimization, (3) Field-synchronous scheduling, and (4) System development and rollout.