Machine learning in combinatorial optimization – an application to machine scheduling
- 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.
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| Author: | Aykut UzunogluORCiD |
|---|---|
| URN: | urn:nbn:de:bvb:384-opus4-1119448 |
| Frontdoor URL | https://opus.bibliothek.uni-augsburg.de/opus4/111944 |
| Advisor: | Axel Tuma |
| Type: | Doctoral Thesis |
| Language: | English |
| Date of Publication (online): | 2024/04/16 |
| Year of first Publication: | 2024 |
| Publishing Institution: | Universität Augsburg |
| Granting Institution: | Universität Augsburg, Wirtschaftswissenschaftliche Fakultät |
| Date of final exam: | 2024/02/22 |
| Release Date: | 2024/04/16 |
| Tag: | Serial-batching |
| GND-Keyword: | Maschinelles Lernen; Operations Research |
| Page Number: | 102 |
| Institutes: | Wirtschaftswissenschaftliche Fakultät |
| Wirtschaftswissenschaftliche Fakultät / Institut für Betriebswirtschaftslehre | |
| Wirtschaftswissenschaftliche Fakultät / Institut für Betriebswirtschaftslehre / Lehrstuhl für Production & Supply Chain Management | |
| Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
| Licence (German): |  Deutsches Urheberrecht |
