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Abdulrahman Nahhas
Abdulrahman Nahhas
Research Associat at the Very Larg Business Application Lab, University of Magdeburg
Email verificata su ovgu.de - Home page
Titolo
Citata da
Citata da
Anno
Simulation and the emergency department overcrowding problem
A Nahhas, A Awaldi, T Reggelin
Procedia Engineering 178, 368-376, 2017
412017
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
342020
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
332021
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
262021
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
232016
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
222019
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
162018
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
122022
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
112020
Toward a lifecycle for data science: a literature review of data science process models
C Haertel, M Pohl, A Nahhas, D Staegemann, K Turowski
92022
An adaptive scheduling framework for solving multi-objective hybrid flow shop scheduling problems
A Nahhas, M Krist, K Turowski
92021
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
82020
Heuristic and metaheuristic simulation-based optimization for solving a hybrid flow shop scheduling problem
A Nahhas, P Aurich, T Reggelin, J Tolujew
82016
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
72022
A Preliminary Overview of the Situation in Big Data Testing.
D Staegemann, M Volk, M Pohl, R Häusler, A Nahhas, M Abdallah, ...
IoTBDS, 296-302, 2021
62021
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
62020
Proof of Provision: Improving Blockchain Technology by Cloud Computing.
M Pohl, A Nahhas, S Bosse, K Turowski
CLOSER, 523-527, 2019
62019
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
52023
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
52023
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
52022
Il sistema al momento non può eseguire l'operazione. Riprova più tardi.
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