Dr. Janis Brammer
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| Vita | ||||||
| Since 11/2017 |
Doctoral candidate, Volkswagen AG, Wolfsburg |
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| 10/2017 |
Master's thesis: „Prediction of reconditioning costs of direct shift gear boxes at the example of the after sales enterprise at Volkswagen AG” |
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| 04/2017 – 10/2017 |
Intern, Volkswagen AG, Kassel |
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| 10/2016 – 01/2017 |
Semester abroad, École d'Ingénieur Généraliste en Informatique, Paris |
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| 11/2015 – 09/2016 |
Working student, Deloitte Consulting GmbH, Hannover |
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| 10/2015 – 10/2017 |
Master of Science in Business Information Systems, Georg-August-Universität Göttingen |
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| 08/2015 |
Bachelor's thesis: „Predicting the success of open source projects“ |
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| 07/2015 – 10/2015 |
Intern, Deloitte Consulting GmbH, Hannover |
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10/2012 – 08/2015 |
Bachelor of Science in Business Information Systems, Georg-August-Universität |
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| 10/2016 – 09/2017 |
Deutschlandstipendium |
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Scientific publications
Journal Articles
Years: 2025 | 2022 | 2021
back to the top of all publications2025
back to the year overview- Stappert M, Lutz B, Brammer J, Neumann D
Solving the Paint Shop Problem with Flexible Management of Multi-Lane Buffers Using Reinforcement Learning and Action Masking
2025 European Journal of Operational Research, volume: Forthcoming
2022
back to the year overview- Brammer J, Lutz B, Neumann D
Permutation Flow Shop Scheduling with Multiple Lines and Demand Plans Using Reinforcement Learning
2022 European Journal of Operational Research, volume: 299, issue: 1, pages: 75 - 86 - Brammer J, Lutz B, Neumann D
Stochastic mixed model sequencing with multiple stations using reinforcement learning and probability quantiles
2022 OR Spectrum, volume: 44, issue: 1, pages: 29 - 56
2021
back to the year overview- Brammer J, Lutz B, Neumann D
Solving the mixed model sequencing problem with reinforcement learning and metaheuristics
2021 Computers & Industrial Engineering, volume: 162, issue: 107704
Talks
Years: 2019 back to the top of all publications2019
back to the year overview- Brammer J, Lutz B, Neumann D
Flow-Shop Scheduling with Demand Plans and Multiple Lines Using Reinforcement Learning
2019 Conference on Information Systems and Technology, Seattle, USA
Credits: SILK Icons by http://www.famfamfam.com/lab/icons/silk/