Titlebook: Machine Learning-based Prediction of Missing Parts for Assembly; Fabian Steinberg Book 2024 The Editor(s) (if applicable) and The Author(s

MIRE

,Publication I: Approaches for the Prediction of Lead Times in an Engineer to Order Environment—a Syts with typically a large number of individual parts and complex production processes. A multitude of approaches have been proposed in the literature for predicting lead times considering different data and methods or algorithms from operations research (OR) and machine learning (ML). In order to pr

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Critical Refection and Future Perspective,tematic literature review in thesis I to get a comprehensive overview of ML and OR approaches for lead time prediction, ML models for the prediction of missing parts at the start of assembly from in-house production and component procurement are presented in theses II—IV. However, the results within

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Fabian Steinbergy may be quite variable. Species have different habitat preferences that further accentuate spatial and temporal heterogeneity. Zooplankton are major herbivores as well as important predators in aquatic ecosystems. Therefore, to understand lake metabolism it is necessary to evaluate the biomass and

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Fabian Steinbergr the synthesis of scientific data, and (2) to support effective management of water resources by providing reliable predictive frameworks. These research and management purposes are mutually consistent on a long-term basis, as research advancements have led to improved capabilities and credibility

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