(2021, funded by the DAAD under the RISE program, supervisor: Prof. Dr. Matthias Mnich, internship provider: Tobias Stamm MSc, intern: Chloe Jones)
Manufacturers continuously face the challenge of optimizing their production process, to reduce lead times and energy costs, and improves customer satisfaction. Shortened product life cycles and a rising demand for custom-made products increase the complexity of efficient planning and control of modern manufacturing systems.
Abstract models of such planning tasks are often well-investigated in the scientific literature. However, much of this research either shows that such tasks are inherently intractable and cannot be solved efficiently, or proposes algorithmic tools that are way too complicated to be useful in practice. Therefore, planners are left with developing their own ad-hoc optimization methods, risking re-inventing the wheel and not benefiting from state-of-the-art knowledge, and giving away much optimization potential which directly leads to reduced profits. Our research aims at translating state-of-the-art algorithms for scheduling and planning into powerful, stable, versatile and easy-to-use tools for optimizers in manufacturing industries.