| 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 | 185 | 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 | 81 | 2020 |
| Impact assessment model for the implementation of cargo bike transshipment points in urban districts T Assmann, S Lang, F Müller, M Schenk Sustainability 12 (10), 4082, 2020 | 62 | 2020 |
| Mixed reality in production and logistics: Discussing the application potentials of Microsoft HoloLensTM S Lang, MSSD Kota, D Weigert, F Behrendt Procedia Computer Science 149, 118-129, 2019 | 57 | 2019 |
| 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 | 49 | 2020 |
| 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 | 46 | 2021 |
| 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 | 36 | 2017 |
| 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 | 35 | 2021 |
| 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 | 27 | 2018 |
| Integration of LiFi technology in an industry 4.0 learning factory VD Mukku, S Lang, T Reggelin Procedia Manufacturing 31, 232-238, 2019 | 24 | 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 | 24 | 2018 |
| A two-stage RNN-based deep reinforcement learning approach for solving the parallel machine scheduling problem with due dates and family setups F Li, S Lang, B Hong, T Reggelin Journal of Intelligent Manufacturing 35 (3), 1107-1140, 2024 | 22 | 2024 |
| 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 | 21 | 2021 |
| Toward adaptive manufacturing: Scheduling problems in the context of industry 4.0 A Nahhas, S Lang, S Bosse, K Turowski 2018 Sixth international conference on enterprise systems (ES), 108-115, 2018 | 19 | 2018 |
| A brief introduction to deploy Amazon Web Services for online discrete-event simulation W Hofmann, S Lang, P Reichardt, T Reggelin Procedia Computer Science 200, 386-393, 2022 | 17 | 2022 |
| 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 | 16 | 2020 |
| Towards learning-and knowledge-based methods of artificial intelligence for short-term operative planning tasks in production and logistics: research idea and framework S Lang, M Schenk, T Reggelin IFAC-PapersOnLine 52 (13), 2716-2721, 2019 | 15 | 2019 |
| Metamodelling of inventory-control simulations based on a multilayer perceptron I Jackson, J Tolujevs, S Lang, Z Kegenbekov Transport and Telecommunication Journal 20 (3), 251-259, 2019 | 14 | 2019 |
| Integration of the a2c algorithm for production scheduling in a two-stage hybrid flow shop environment FT Gerpott, S Lang, T Reggelin, H Zadek, P Chaopaisarn, S Ramingwong Procedia Computer Science 200, 585-594, 2022 | 11 | 2022 |
| Mesoscopic simulation models for logistics planning tasks in the automotive industry S Lang, T Reggelin, T Wunder Procedia Engineering 178, 298-307, 2017 | 10 | 2017 |
