| 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 | 48 | 2020 |
| Simulation and the emergency department overcrowding problem A Nahhas, A Awaldi, T Reggelin Procedia Engineering 178, 368-376, 2017 | 48 | 2017 |
| 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 | 45 | 2021 |
| 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 |
| Comparative analysis of machine learning models for anomaly detection in manufacturing A Kharitonov, A Nahhas, M Pohl, K Turowski Procedia Computer Science 200, 1288-1297, 2022 | 28 | 2022 |
| Exploring the specificities and challenges of testing big data systems D Staegemann, M Volk, A Nahhas, M Abdallah, K Turowski 2019 15th International Conference on Signal-Image Technology & Internet …, 2019 | 25 | 2019 |
| Simulation-based optimization for solving a hybrid flow shop scheduling problem P Aurich, A Nahhas, T Reggelin, J Tolujew 2016 Winter Simulation Conference (WSC), 2809-2819, 2016 | 25 | 2016 |
| Toward a lifecycle for data science: a literature review of data science process models C Haertel, M Pohl, A Nahhas, D Staegemann, K Turowski | 19 | 2022 |
| 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 |
| 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 |
| On the Integration of Google Cloud and SAP HANA for Adaptive Supply Chain in Retailing A Nahhas, C Haertel, C Daase, M Volk, A Ramesohl, H Steigerwald, ... Procedia Computer Science 217, 1857-1866, 2023 | 13 | 2023 |
| Following the digital thread–a cloud-based observation C Daase, C Haertel, A Nahhas, M Volk, H Steigerwald, A Ramesohl, ... Procedia Computer Science 217, 1867-1876, 2023 | 12 | 2023 |
| Challenges in Data Acquisition and Management in Big Data Environments. D Staegemann, M Volk, A Saxena, M Pohl, A Nahhas, R Häusler, ... IoTBDS, 193-204, 2021 | 11 | 2021 |
| Deep reinforcement learning techniques for solving hybrid flow shop scheduling problems: Proximal policy optimization (PPO) and asynchronous advantage actor-critic (A3C) A Nahhas, A Kharitonov, K Turowski | 10 | 2022 |
| An adaptive scheduling framework for solving multi-objective hybrid flow shop scheduling problems A Nahhas, M Krist, K Turowski | 10 | 2021 |
| Heuristic and metaheuristic simulation-based optimization for solving a hybrid flow shop scheduling problem A Nahhas, P Aurich, T Reggelin, J Tolujew | 10 | 2016 |
| Towards a Decision Support System for Big Data Projects. M Volk, D Staegemann, S Bosse, A Nahhas, K Turowski, N Gronau, ... Wirtschaftsinformatik (Zentrale Tracks), 357-368, 2020 | 8 | 2020 |
| MLOps in Data Science Projects: A Review C Haertel, D Staegemann, C Daase, M Pohl, A Nahhas, K Turowski 2023 IEEE International Conference on Big Data (BigData), 2396-2404, 2023 | 7 | 2023 |
| Hybrid approach for solving multi-objective hybrid flow shop scheduling problems with family setup times A Nahhas, A Kharitonov, A Alwadi, K Turowski Procedia Computer Science 200, 1685-1694, 2022 | 7 | 2022 |
| Determining Potential Failures and Challenges in Data Driven Endeavors: A Real World Case Study Analysis. D Staegemann, M Volk, T Vu, S Bosse, R Häusler, A Nahhas, M Pohl, ... IoTBDS, 453-460, 2020 | 7 | 2020 |
Sascha BosseResearcher, Very Large Business Applications Lab - University of MagdeburgEmail yang diverifikasi di ovgu.de
