Schaumann, Sarah

Sarah Schaumann

Contact

Sarah Schaumann
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ETH Zurich
Chair of Logistics Management
Weinbergstrasse 56/58
WEV F118
8092 Zurich
Schweiz

Sarah Schaumann is a research associate and Ph.D. candidate at the Chair of Logistics Management since August 2020. She holds a Bachelor and Master of Science in Mechanical Engineering and Business Administration from Technical University of Darmstadt, Germany. During her master studies, she spent an academic exchange semester at the National University of Singapore (NUS). In the course of her student research project (title: Considering Times Windows for Last-mile Delivery and First-mile Pickup Integrated Routes in Urban Distribution Networks) she spent six months at ETH Zurich, Chair of Logistics Management. Sarah Schaumann wrote her master thesis (title: Potential analysis for AI-based control optimization of the central cooling system at the Bosch Rexroth plant Schweinfurt) at TU Darmstadt, research group ETA (Energy Technologies and Applications in Production).

She gained production and supply chain related work experience at Roche Diagnostics in Mannheim (Germany) and part-time at Porsche AG in Zuffenhausen (Germany). Further, she gained experience in academia as a student assistant at the institute of applied dynamics and the department of mathematics, both at TU Darmstadt. During her studies, she was an active member of the German and European associations for Industrial Engineers (VWI and ESTIEM).

Selected Publications

  • Schaumann, Sarah K./Thakur-Weigold, Bublu/Van Wassenhove, Luk N. (2024): Reconciling Rigor vs. Relevance: Lessons from Humanitarian Fleet Management. Production and Operations Management, doi: external page10.1177/10591478241245977
  • Schaumann, Sarah K./Bergmann, Felix M./Wagner, Stephan M./Winkenbach, Matthias (2023): Route Efficiency Implications of Time Windows and Vehicle Capacities in First- and Last-Mile Logistics, European Journal of Operational Research, Vol. 311, No. 1, November, pp. 88-111, doi: external page10.1016/j.ejor.2023.04.018
  • Weigold, Matthias/Ranzau, Heiko/Schaumann, Sarah/Kohne, Thomas/Panten, Niklas/Abele, Eberhard (2021): Method for the Application of Deep Reinforcement Learning for Optimised Control of Industrial Energy Supply Systems by the Example of a Central Cooling System, CIRP Annals, Vol. 70, No. 1, pp. 17-20, doi: external page10.1016/j.cirp.2021.03.021
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