Karl F. Doerner
Papers
3
Total Citations
55
H-Index
3
About
Karl F. Doerner is a leading researcher in operations research and production management, with a primary focus on the integration of collaborative robots (cobots) into manufacturing systems. His work addresses the critical challenge of optimizing cobot assignment alongside complex job shop scheduling problems, blending human-robot collaboration with advanced metaheuristic solution approaches. Doerner’s major contributions include developing hybrid metaheuristics and biased random-key genetic algorithms that enable efficient cobot placement and task scheduling, directly improving production performance and worker safety. His most cited paper, "A hybrid metaheuristic solution approach for the cobot assignment and job shop scheduling problem" (2022), has garnered 26 citations, reflecting its timely relevance. He has also advanced the field by combining metaheuristics with process mining techniques to refine cobot placement strategies. Doerner’s research is notable for its practical impact on modern manufacturing, where cobots—requiring no safety distance and offering fast setup—are increasingly vital. His work is essential reading for students and researchers interested in the intersection of automation, human factors, and optimization in smart factories.
Research Focus
Key Achievements
Top Papers
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