Application of machine learning in production scheduling

Abstract

For human experts, it is often too hard or too time-consuming to manually detect patterns in big data sets. Machine learning is applied in many areas to detect such patterns. Its applications are by no means limited to research, as machine learning also plays a big role in the industrial sector. This thesis applies machine learning to the following two topics. The first part of the thesis deals with product quality classification for automotive paint shops. The second part investigates automated parameter configuration for dispatching rules that are used in machine scheduling. We use a binary classification to predict the product quality of an automotive paint shop based on its scheduling data. We propose a set of features to characterize the production process. These features are used to classify whether or not the quality of the product is satisfactory. We can show that the best model we found performs better than a baseline model on an unseen data set. In the automated parameter configuration part of the thesis, we investigate machine learning methods based on multi-target regression to automatically configure dispatching rules for real-life planning scenarios where multiple objectives are considered. We propose a novel set of features to characterize instances of the parallel machine scheduling problem, and describe how supervised learning can be used to obtain optimized parameter configurations for given machine scheduling instances. Experimental results show that our approach can obtain high-quality solutions for real-life scheduling scenarios in short run times.