Adaptive Management Shell for Mapping the Process Capability of Manufacturing Components: A Systematic Mapping Study. - David Heik; Javad Ghofrani; Dirk Reichelt (INDIN, 2020)

An Application of Reinforcement Learning in Industrial Cyber-Physical Systems. - David Heik; Fouad Bahrpeyma; Dirk Reichelt (OVERLAY@AI*IA, 2022)

Dynamic Job Shop Scheduling in an Industrial Assembly Environment Using Various Reinforcement Learning Techniques. - David Heik; Fouad Bahrpeyma; Dirk Reichelt (ISDA (3), 2022)

Solving a Dynamic Scheduling Problem for a Manufacturing System with Reinforcement Learning. - David Heik; Fouad Bahrpeyma; Dirk Reichelt (IntelliSys (2), 2023)

Application of Multi-agent Reinforcement Learning to the Dynamic Scheduling Problem in Manufacturing Systems. - David Heik; Fouad Bahrpeyma; Dirk Reichelt (LOD (2), 2023)

Ganzheitliche Wandlungsfähigkeit von Produktionssystemen - der Schlüssel zur Ressourcenwende? - Pia Bielitz; Nicole Jäpel; David Heik; Dirk Reichelt (HMD Prax. Wirtsch., 2023)

Application of Inhomogeneous QMIX in Various Architectures to Solve Dynamic Scheduling in Manufacturing Environments. - David Heik; Alexander Böhm 006; Fouad Bahrpeyma; Dirk Reichelt (INDIN, 2024)