Automatische Generierung adaptiver Modelle zur Simulation von Produktionssystemen

The simulation of production and logistics processes is used today in a variety of industries. Simulation is used for the analysis, design, and optimization of production and logistics processes and their resource requirements and can be used here both in the planning, commissioning, and during the actual operation.The undisputed great potentials of material flow simulation stand against high costs and effort for implementing simulation models and conducting simulation studies. Due to the poor integration and standardization of the simulation and increasing demands of companies with respect to accuracy, flexibility, adaptability, speed, cost, and reusability the expenses for using simulation are increasing.One approach that has been attempted repeatedly for several years as a contribution to mitigate the cost of using simulation is the automatic generation of simulation models. Automatic model generation refers to different approaches permitting simulation models or parts of models to be produced by means of algorithms. So far, no approach has been published which yields good results for a broad spectrum of application areas and industries.In this work, a comprehensive framework for the integration and automation of the simulation was designed and validated. The framework consists of organizational, methodical, and prototypically technical components. In this context, it is argued that for a broad application of automatic model generation the use of standards is required. Specifically, the Core Manufacturing Simulation Data (CMSD) is proposed as useful standard and a reference implementation of the standard provides the basis for the entire work. The support of all simulation phases, i.e. not only model building but also the evaluation of alternatives, initialization, evaluation of results, etc. is ensured throughout the entire framework. Furthermore, model generation methods and procedures for representing dynamic behavior in simulation models were specifically classified and selected methods were implemented and presented.Ein Ansatz, der seit einigen Jahren immer wieder als ein Lösungsbeitrag für eine bessere Nutzung der Simulation von Produktionsprozessen gerade in KMU’s betrachtet wird, ist die automatische Generierung von Simulationsmodellen. In dieser Arbeit wird ein umfassendes Rahmenwerk zur Integration bzw. Automatisierung der Simulation vorgestellt. Es wurden organisatorische, methodische als auch prototypisch technische Komponenten entworfen und validiert. Hierbei wird die These vertreten, dass eine breit anwendbare automatische Modellgenerierung allein durch die Nutzung von Standards zum Datenaustausch bzw. zur Konzeptmodellerstellung sinnvoll zu implementieren ist. Konkret wurde der Core Manufacturing Simulation Data (CMSD) Standard genutzt bzw. bildet dessen Referenzanwendung die Basis der Arbeit. Die Unterstützung aller Simulationsphasen, d.h. nicht allein der Modellerstellung sondern auch der Alternativenbildung, Initialisierung, Ergebnisauswertung usw. wird in allen Komponenten durchgehend gewährleistet. Weiterhin wurden konkret Modellgenerierungsmethoden und Verfahren zur Abbildung des dynamischen Verhaltens in Modellen klassifiziert und einzelne Lösungsansätze vorgestellt.Auch im Buchhandel erhältlich: Automatische Generierung adaptiver Modelle zur Simulation von Produktionssystemen / Sören Bergmann Ilmenau : Univ.-Verl. Ilmenau, 2014. XXXVII, 221 S. ISBN 978-3-86360-084-6 Preis: 31,20

Prädiktiv-reaktives Scheduling zur Steigerung der Robustheit in der Matrix-Produktion

Due to the increasing individualization of products, manufacturing companies are offering more and more variants with decreased quantities per variant. In addition, customer demand is becoming more volatile and difficult to predict. The main challenge is to eco-nomically produce a fluctuating mix of variants with fluctuating total quantities. Matrix-Production systems aiming for a production in batch size 1 decoupled from a takt are therefore a current object of research. In addition to the design of these systems, an increasingly important role is filled by production planning and control, since the material flows in such production systems are highly complex. The state of research is characterized by a multitude of predictive-reactive methods for scheduling even in complex production systems. However, there is no approach that specifically considers robustness in predictive planning in order to enable reactive rescheduling to maintain the desired logistical performance despite unforeseen disruptions. Therefore, a method for predictive-reactive product control of matrix-structured produc-tion systems was developed in this thesis, which allows the determination of an optimal degree of robustness in predictive robust scheduling and thus enables an optimal mix of prevention and reaction in production control. The method consists of three parts: First, in predictive robust scheduling, a schedule is generated on the basis of the pro-duction program, in which a desired extent of slip times between processing steps is then inserted. The robust schedules are then carried out in a discrete-event simulation. In the event of longer disturbances, a rescheduling corridor is determined secondly, which indicates which processing steps of which orders must be rescheduled depending on the duration of the disturbance and the underlying schedule. The rescheduling corridors are then rescheduled thirdly in reactive rescheduling and the results are transferred to the discrete event simulation for reintegration. Reactive rescheduling uses reinforcement learning based on a decentralized Markov process to learn optimal selection strategies for orders depending on the station. The method was tested in an application for a concept of a flexible body-in-white production with a partner from the automotive industry. The developed method contributes to the understanding of the concept of robustness as well as to the application possibilities and limits of reinforcement learning in production control. To the author’s knowledge, the work is the first approach to integrate robustness considerations directly into predictive-reactive scheduling approaches in order to improve the logistical performance