| A review on reinforcement learning algorithms and applications in supply chain management B Rolf, I Jackson, M Müller, S Lang, T Reggelin, D Ivanov International Journal of Production Research 61 (20), 7151-7179, 2023 | 75 | 2023 |
| Integration of Deep Reinforcement Learning and Discrete-Event Simulation for Real-Time Scheduling of a Flexible Job Shop Production S Lang, F Behrendt, N Lanzerath, T Reggelin, M Müller 2020 Winter Simulation Conference (WSC), 3057-3068, 2020 | 64 | 2020 |
| Simulation and the Emergency Department Overcrowding Problem A Nahhas, A Awaldi, T Reggelin Procedia Engineering 178, 368-376, 2017 | 41 | 2017 |
| Mesoscopic supply chain simulation T Hennies, T Reggelin, J Tolujew, PA Piccut Journal of Computational Science 5 (3), 463-470, 2014 | 40 | 2014 |
| Open-source discrete-event simulation software for applications in production and logistics: An alternative to commercial tools? S Lang, T Reggelin, M Müller, A Nahhas Procedia Computer Science 180, 978-987, 2021 | 35 | 2021 |
| Assigning dispatching rules using a genetic algorithm to solve a hybrid flow shop scheduling problem B Rolf, T Reggelin, A Nahhas, S Lang, M Müller Procedia Manufacturing 42, 442-449, 2020 | 34 | 2020 |
| Integrating Virtual Commissioning Based on High Level Emulation into Logistics Education W Hofmann, S Langer, S Lang, T Reggelin Procedia Engineering 178, 24-32, 2017 | 34 | 2017 |
| A mesoscopic approach to modeling and simulation of logistics processes T Reggelin, J Tolujew Proceedings of the 2011 Winter Simulation Conference (WSC), 1508-1518, 2011 | 34 | 2011 |
| The combination of discrete-event simulation and genetic algorithm for solving the stochastic multi-product inventory optimization problem I Jackson, J Tolujevs, T Reggelin Transport and Telecommunication Journal 19 (3), 233-243, 2018 | 29 | 2018 |
| NeuroEvolution of augmenting topologies for solving a two-stage hybrid flow shop scheduling problem: A comparison of different solution strategies S Lang, T Reggelin, J Schmidt, M Müller, A Nahhas Expert Systems with Applications 172, 114666, 2021 | 28 | 2021 |
| Mesoskopische Modellierung und Simulation logistischer Flusssysteme T Reggelin Magdeburg, Universität, Diss., 2011, 2011 | 25 | 2011 |
| A mesoscopic approach to the simulation of logistics systems M Schenk, J Tolujew, T Reggelin Advanced manufacturing and sustainable logistics, 15-25, 2010 | 24 | 2010 |
| Simulation-based optimization for solving a hybrid flow shop scheduling problem P Aurich, A Nahhas, T Reggelin, J Tolujew Proceedings of the 2016 Winter Simulation Conference, 2809-2819, 2016 | 23 | 2016 |
| Simulation and Virtual Commissioning of Modules for a Plug-and-Play Conveying System W Hofmann, JH Ulrich, S Lang, T Reggelin, J Tolujew IFAC-PapersOnLine 51 (11), 649-654, 2018 | 22 | 2018 |
| Integration of LiFi Technology in an Industry 4.0 Learning Factory VD Mukku, S Lang, T Reggelin Procedia Manufacturing 31, 232-238, 2019 | 21 | 2019 |
| Towards a Modular, Decentralized and Digital Industry 4.0 Learning Factory S Lang, T Reggelin, M Jobran, W Hofmann 2018 Sixth International Conference on Enterprise Systems (ES), 123-128, 2018 | 20 | 2018 |
| Mesoscopic modeling and simulation of logistics networks M Schenk, J Tolujew, T Reggelin IFAC Proceedings Volumes 42 (4), 582-587, 2009 | 20 | 2009 |
| Modeling Production Scheduling Problems as Reinforcement Learning Environments based on Discrete-Event Simulation and OpenAI Gym S Lang, M Kuetgens, P Reichardt, T Reggelin IFAC-PapersOnLine 54 (1), 793-798, 2021 | 15 | 2021 |
| Mesoskopische Simulation von Flusssystemen—algorithmisch steuern und analytisch berechnen M Schenk, J Tolujew, T Reggelin Beiträge zu einer Theorie der Logistik, 463-485, 2008 | 14 | 2008 |
| Evolving Neural Networks to Solve a Two-Stage Hybrid Flow Shop Scheduling Problem with Family Setup Times S Lang, T Reggelin, F Behrendt, A Nahhas Proceedings of the 53rd Hawaii International Conference on System Sciences, 2020 | 13 | 2020 |
Sebastian LangAI Researcher at Fraunhofer IFFovgu.de의 이메일 확인됨
