Bernd Waschneck
Impact in
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- Scheduling and Optimization Algorithms
- Digital Transformation in Industry
- Advanced Manufacturing and Logistics Optimization
- Assembly Line Balancing Optimization
- Flexible and Reconfigurable Manufacturing Systems
- Manufacturing Process and Optimization
- Management Information Systems top 10%
Papers in
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- Stochastic Gradient Optimization Techniques 3
- Reinforcement Learning in Robotics 2
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- Advanced Neural Network Applications 6
- Co-authors
- Thomas Altenmüller (6 shared papers)Thomas Bauernhansl (4 shared papers)Andreas Kyek (4 shared papers)André Reichstaller (2 shared papers)Lenz Belzner (2 shared papers)Alexander Knapp (2 shared papers)Andreas Kuhnle (2 shared papers)Gisela Lanza (2 shared papers)
In The Last Decade
Bernd Waschneck
14 papers receiving 497 citations
Bernd Waschneck's Hit Papers
Peers
Comparison fields: 5 of 72
- Industrial and Manufacturing Engineering 388
- Management Information Systems 41
- Control and Systems Engineering 93
- Safety, Risk, Reliability and Quality 28
- Medical Laboratory Technology 4
Countries citing papers authored by Bernd Waschneck
This map shows the geographic impact of Bernd Waschneck's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Bernd Waschneck with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bernd Waschneck more than expected).
Fields of papers citing papers by Bernd Waschneck
This network shows the impact of papers produced by Bernd Waschneck. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Bernd Waschneck. The network helps show where Bernd Waschneck may publish in the future.
Co-authors
The 13 scholars most cited alongside Bernd Waschneck, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Optimization of global production scheduling with deep reinforcement learning Hit paper breakdown → | 2018 | 264 |
| 2 | 2018 | 84 | |
| 3 | 2020 | 56 | |
| 4 | 2022 | 51 | |
| 5 | 2017 | 29 | |
| 6 | 2024 | 9 | |
| 7 | 2021 | 5 | |
| 8 | 2022 | 5 | |
| 9 | 2022 | 3 | |
| 10 | 2024 | 2 | |
| 11 | 2024 | 2 | |
| 12 | 2022 | 2 | |
| 13 | 2018 | 1 | |
| 14 | 2023 | 1 | |
| 15 | 2024 | 0 | |
| 16 | 2022 | 0 |
About Bernd Waschneck
Bernd Waschneck is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering, Hardware and Architecture and Computer Networks and Communications, having authored 16 papers that have together received 514 indexed citations. Recurring topics across this work include Scheduling and Optimization Algorithms (6 papers), Advanced Neural Network Applications (6 papers), Parallel Computing and Optimization Techniques (4 papers), Advanced Manufacturing and Logistics Optimization (3 papers), Stochastic Gradient Optimization Techniques (3 papers), Radiation Effects in Electronics (2 papers), Reinforcement Learning in Robotics (2 papers) and Assembly Line Balancing Optimization (2 papers). The work is most often cited by research in Industrial and Manufacturing Engineering (388 citations), Management Information Systems (41 citations), Control and Systems Engineering (93 citations), Safety, Risk, Reliability and Quality (28 citations) and Medical Laboratory Technology (4 citations). Bernd Waschneck has collaborated with scholars based in Germany and Canada. Frequent co-authors include Thomas Altenmüller, Thomas Bauernhansl, Andreas Kyek, André Reichstaller, Lenz Belzner, Alexander Knapp, Andreas Kuhnle, Gisela Lanza, Marvin Carl May and Akash Kumar. Their work appears in journals such as Journal of Systems Architecture, Electronics, International Journal of Neural Systems, IEEE Access and Journal of Manufacturing Systems.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.