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Technical University of Munich

Alexander Pahr

Doctoral candidate

Email: alexander.pahr(at)tum.de
Phone: +49 (0)89 289 24878
Room: 1531 (building 0505)
 

Research interests

  • Mathematical modeling and optimization
  • Deep Reinforcement Learning
  • Food industry

Education & Employment

Since 03/2019Research associate
Chair of Production & Supply Chain Management,
Technical University of Munich, Germany
10/2016 - 03/2020B.Sc. in Information Systems
Technical University of Munich, Germany
10/2016 - 03/2019M.Sc. in Management and Technology
Technical University of Munich, Germany
10/2012 - 03/2016B.Sc. in Global Business Management
University of Augsburg, Germany

Publications

  • Pahr, A.; Grunow, M.; Amorim, P.: Learning from the aggregated optimum: Managing port wine inventory in the face of climate risks. European Journal of Operational Research 323 (2), 2025, 671-685 more…
  • Fügener, Andreas; Pahr, Alexander; Brunner, Jens O.: Mid-term nurse rostering considering cross-training effects. International Journal of Production Economics 196, 2018, 176-187 more…

Conference contributions

  • Doerr, J; Pahr, A.; Grunow, M.: Exploring the Benefits of Dynamic Scheduling in Complex Manufacturing Settings. (Talk / 8th Stochastic Modelling Meeting (Milano)) 2024 more…
  • Pahr, Alexander; Grunow, Martin: The Value of Blending – Managing Ameliorating Food Inventory Using Deep Reinforcement Learning. (Talk / 8th Stochastic Modelling Meeting, Milan) 2024 more…
  • Doerr, J; Pahr, A.; Grunow, M.: Exploring the Benefits of Dynamic Scheduling in Complex Manufacturing Settings. (Talk / GPOM Summer 2024) 2024 more…
  • Kolemesina, Anna; Pahr, Alexander; Grunow, Martin: Deep Reinforcement Learning for Aging Cheese Inventory Management. (Talk / International Conference on Operations Research) 2024 more…
  • Pahr, Alexander; Grunow, Martin: Managing Ameliorating Food Inventory Using Deep Reinforcement Learning. International Society for Inventory Research 2023 Summer School, 2023Cardiff more…
  • Grunow, Martin; Pahr, Alexander, Amorim, Pedro: Managing ameliorating food with machine learning (Keynote).  6th International Conference on Food and Wine Supply Chains, 2022Bologna, Italy more…
  • Pahr, Alexander; Grunow, Martin; Amorim, Pedro: Learning from the Aggregated Optimum: Decision Rules for Managing Ameliorating Food Inventory. MSOM Conference 2022, SIG Day, 2022Munich, Germany more…
  • Pahr, A.: Deriving interpretable decision rules from optimal purchasing and blending policies in port wine inventory management. (Talk / GPOM online workshop) 2020 more…

Student project supervisions

  • Daniel Postica: Blood Platelet Inventory Management Using Deep Reinforcement Learning. , 2024 more…
  • Luisa Beck: Constraint Programming for Biopharmaceutical Production Scheduling. , 2024 more…
  • Minyu Wu: Deep Reinforcement Learning for the Management of Ameliorating Inventory. , 2024 more…
  • Maximilian Raupenstrauch: Dynamic Flow Shop Scheduling Using a Rolling-Horizon Constraint Programming Approach. , 2024 more…
  • Florian Minnameier: Learning Dispatching Rules in Flow Shop Scheduling: A Gradient-Refined Genetic Programming Approach. , 2024 more…
  • Anna Kolemesina: Deep Reinforcement Learning for Inventory Management of Ameliorating Products: A Cheese Case Study with Focus on Integrating Stochastic Purchase and Sales Prices Using Ornstein-Uhlenbeck Process. Master thesis, 2024 more…
  • Camps Bas, Ignacio: Dynamic Available-to-Promise for the outbound logistics - a case study on the semiconductor supply chain. , 2022 more…
  • Dörr, Jan-Niklas: Genetic programming learning to solve detailed scheduling using dispatching rules. Master thesis, 2022 more…
  • Dao, Thu Ha: Deep Reinforcement Learning for Multi-Age Inventory Management with Complex Production Actions. , 2022 more…
  • Sreekumar, Pavithra: Production planning optimization in a rolling horizon environment for capacitated multi-stage blade production systems under demand uncertainty. Master thesis, 2021 more…
  • Liegel, Silvia: Digitalization of Supply Chain Processes through Robotic Process Automation: A Case from the Electronics Industry. Master thesis, 2020 more…
  • Filatova, Anna: Robotic Process Automation (RPA) and its potential for improvement of process performance based on a case study in Supply Chain Management. Master thesis, 2020 more…
  • Hintermaier, Christina: Using Markov Decision Processes for optimal port wine purchasing and sales considering taste-wise blending incompatibilities. Master thesis, 2020 more…
  • Heisig, Katharina: Using optimal policies of a Markov Decision Process to derive general decision rules for multi-age inventory management of port wine. Master thesis, 2020 more…
  • Hafiz, A.: Comparison of static and dynamic pricing strategies for the short-term order compensation cost process. Master thesis, 2019 more…
  • Riess, A.: Modelling of layout segmentation in flexible production systems. Master thesis, 2019 more…
  • Köpper, J.: Simulation-based performance analysis of assembly line supply systems in the automotive industry: a comparison of autonomous transportation systems and towing trains. Master thesis, 2019 more…

  • Samuel Schott: Deep Reinforcement Learning for Perishable Inventory Management. , 2023 more…
  • Pfeffer, Leon: Selecting Shareable Tasks: Two-Stage Stochastic Programming for Dynamic Assembly Line Balancing. , 2022 more…
  • Postica, Daniel: Interpretable ordering and issuance rules for blood platelet inventory management using decision trees. Bachelor thesis, 2021 more…
  • Zhang, Che: A reinforcement learning approach for inventory management in multi-age food systems. Bachelor thesis, 2020 more…
  • Kern, F.: A simulation of optimal Markov decision process policies - port wine blending and storage management. Bachelor thesis, 2019 more…

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