@phdthesis{Panzer2024,
  author    = {Panzer, Marcel},
  title     = {Design of a hyper-heuristics based control framework for modular production systems},
  doi       = {10.25932/publishup-63300},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-633006},
  school      = {Universit{\"a}t Potsdam},
  pages     = {vi, 334},
  year      = {2024},
  abstract  = {Volatile supply and sales markets, coupled with increasing product individualization and complex production processes, present significant challenges for manufacturing companies. These must navigate and adapt to ever-shifting external and internal factors while ensuring robustness against process variabilities and unforeseen events. This has a pronounced impact on production control, which serves as the operational intersection between production planning and the shop- floor resources, and necessitates the capability to manage intricate process interdependencies effectively. Considering the increasing dynamics and product diversification, alongside the need to maintain constant production performances, the implementation of innovative control strategies becomes crucial. In recent years, the integration of Industry 4.0 technologies and machine learning methods has gained prominence in addressing emerging challenges in production applications. Within this context, this cumulative thesis analyzes deep learning based production systems based on five publications. Particular attention is paid to the applications of deep reinforcement learning, aiming to explore its potential in dynamic control contexts. Analysis reveal that deep reinforcement learning excels in various applications, especially in dynamic production control tasks. Its efficacy can be attributed to its interactive learning and real-time operational model. However, despite its evident utility, there are notable structural, organizational, and algorithmic gaps in the prevailing research. A predominant portion of deep reinforcement learning based approaches is limited to specific job shop scenarios and often overlooks the potential synergies in combined resources. Furthermore, it highlights the rare implementation of multi-agent systems and semi-heterarchical systems in practical settings. A notable gap remains in the integration of deep reinforcement learning into a hyper-heuristic. To bridge these research gaps, this thesis introduces a deep reinforcement learning based hyper- heuristic for the control of modular production systems, developed in accordance with the design science research methodology. Implemented within a semi-heterarchical multi-agent framework, this approach achieves a threefold reduction in control and optimisation complexity while ensuring high scalability, adaptability, and robustness of the system. In comparative benchmarks, this control methodology outperforms rule-based heuristics, reducing throughput times and tardiness, and effectively incorporates customer and order-centric metrics. The control artifact facilitates a rapid scenario generation, motivating for further research efforts and bridging the gap to real-world applications. The overarching goal is to foster a synergy between theoretical insights and practical solutions, thereby enriching scientific discourse and addressing current industrial challenges.},
  language  = {en}
}
@phdthesis{Theuer2022,
  author    = {Theuer, Hanna Katharina},
  title     = {Beherrschung komplexer Produktionsprozesse durch Autonomie},
  doi       = {10.25932/publishup-54184},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-541842},
  school      = {Universit{\"a}t Potsdam},
  pages     = {iv, 297},
  year      = {2022},
  abstract  = {Modern technologies enable the actors of a production process to autonomous decision-making, information-processing, and decision- execution. It devolves hierarchical controlled relationships and distributes decision-making among many actors. Positive consequences include using local competencies and fast on-site action without (time-)consuming cross-process planning run by a central control instance. Evaluating the decentralization of the process helps to compare different control strategies and thus contributes to the mastery of more complex production processes. Although the importance of the communication structure of these actors increases, no method uses this as a basis for operationalizing decentralization. This motivates the focus of this thesis. It develops a three-level evaluation model determining the decentralization of a production process based on two determinants: the communication and decision-making structure of the autonomous actors involved. Based on a definition of decentralization of production processes, it set requirements for a key value that determines the structural autonomy of the actors and selects a suitable social network analysis metric. The possibility of integrated decision-making and decision execution justifies the additional consideration of the decision structure. The differentiation of both factors forms the basis for the classification of actors; the multiplication of both values results in the characteristic value real autonomy describing the autonomy of an actor, which is the key figure of the model's first level. Homogeneous actor autonomy characterizes a high decentralization of the process step, which is the object of consideration of the second level of the model. Comparing the existing with the maximum possible decentralization of the process steps determines the Autonomy Index. This figure operationalizes the decentralization of the process at the third level of the model. A simulation study with two simulation experiments - a central and a decentral controlled process - at Zentrum Industrie 4.0 validates the evaluation model. The application of the model to an industrial production process underlines the practical applicability.},
  language  = {de}
}