Year: 2023
Type: dissertation
Abstract: The dissertation demonstrates the application of Machine Learning models to enhance decision-making in Combinatorial Optimization. An application case from the metal-cutting industry is chosen to verify the applicability of the presented methods. This application case is known as a serial-batch scheduling problem and is NP-hard. Current attempts in the literature solve large-scale instances of this problem using heuristics. The dissertation shows the limitations of the current approach and presents four contributions that enhance the decision-making on different levels.
Institution: University of Augsburg
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7030016500