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In dem ersten Teil dieses Beitrags, welcher in der Industrie 4.0 Management Ausgabe 5/2021 erschienen ist, wurde das Referenzmodell bereits in seinen wesentlichen Grundzügen erläutert [1]. Im zweiten Teil soll die Weiterentwicklung zu einem flexiblen Referenzmodell aufgezeigt werden. Der Fokus liegt auf die Implementierung von weiteren Planungstools, und die Implementierung von KI-Tools zur Erreichung eines dynamischen Produktionsengineerings in Form einer ganzheitlichen und integrierten Fabrikplanung.
Der digitale Zwilling dringt immer weiter in den Fokus von Produktionsunternehmen vor und wurde von Gartner als wichtige Schlüsseltechnologie identifiziert. Volkswagen setzt die Technologie in der Cloud ein, um zukünftig die Produktion an allen Standorten digital zu planen, zu steuern und zu optimieren. Dennoch ist diese Technologie im Mittelstand bisher kaum vertreten. Dieser Beitrag beschreibt ein flexibles Referenzmodell für die Planung und Optimierung der Produktion durch den digitalen Zwilling. Der Fokus liegt zum einen auf der Optimierung statischer Layouts und Materialflüsse und zum anderen auf der Optimierung der dynamischen Materialflüsse und der zeitlichen Organisation von Prozessen.
In the railway technical centers, scheduling the maintenance activities is a very complex task, it consists in ordering, in the time, all the maintenance operations on the workstations, while respecting the number of resources, precedence constraints, and the workstations' availabilities. Currently, this process is not completely automatic. For improving this situation, this paper presents a mathematical model for the maintenance activities scheduling in the case of railway remanufacturing systems. The studied problem is modeled as a flexible job-shop, with the possibility for a job to be executed several times on a stage. MILP formulation is implemented with the Makespan as an objective, representing the time for remanufacturing the train. The aim is to create a generic model for optimizing the planning of the maintenance activities and improving the performance of the railway technical centers. At last, numerical results are presented, discussing the impact of the instances size on the computing time to solve the described problem.
Robust scheduling problem is a major decision problem that is addressed in the literature, especially for remanufacturing systems; this problem is complex because of the high uncertainty and complex constraints involved. Generally, the existing approaches are dedicated to specific processes and do not enable the quick and efficient generation and evaluation of schedules. With the emergence of the Industry 4.0 paradigm, data availability is now considered an opportunity to facilitate the decision-making process. In this study, a data-driven decisionmaking process is proposed to treat the robust scheduling problem of remanufacturing systems in uncertain environments. In particular, this process generates simulation models based on a data-driven modeling approach. A robustness evaluation approach is proposed to answer several decision questions. An application of the decision process in an industrial case of a remanufacturing system is presented herein, illustrating the impact of robustness evaluation results on real-life decisions.
Material flow simulation is a core technology of Industry 4.0. It can analyze and improve large-scale production systems through experimentation with digital simulation models. However, modeling in discrete event simulation is considered as an effortful and time-consuming activity and challenges especially small and medium-sized enterprises. Systematic experiments and what-if-analysis require a large number of models. Modeling and simulation becomes a repetitive activity and the ability to model and simulate instantly becomes crucial for industry, 4.0. However, model generation typically uses specific methods to build models with individual properties for specific physical systems. A general literature review cannot sufficiently describe the current state of model generation. This study aims to provide an analysis of model generation based on the modeling strategy, modeling view, and production system type, as well as model properties and limitations.